Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/jackguagua/awesome-nas-papers
Awesome Neural Architecture Search Papers
https://github.com/jackguagua/awesome-nas-papers
List: awesome-nas-papers
architecture-search autodl automl awesome-list nas neural-architecture-search papers
Last synced: about 2 months ago
JSON representation
Awesome Neural Architecture Search Papers
- Host: GitHub
- URL: https://github.com/jackguagua/awesome-nas-papers
- Owner: jackguagua
- Created: 2020-08-16T03:12:59.000Z (about 4 years ago)
- Default Branch: master
- Last Pushed: 2024-01-31T21:46:21.000Z (8 months ago)
- Last Synced: 2024-05-21T13:19:21.423Z (4 months ago)
- Topics: architecture-search, autodl, automl, awesome-list, nas, neural-architecture-search, papers
- Homepage:
- Size: 152 KB
- Stars: 412
- Watchers: 7
- Forks: 56
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-machine-learning-resources - **[List - nas-papers?style=social) (Table of Contents)
README
# Awesome Neural Architecture Search Papers
[（中文）](https://github.com/jackguagua/awesome-nas-papers/blob/master/README_cn.md)
We would like to maintain a complete list of NAS-related papers and provide a guide for some of the papers that have received wide interest.
### Table of Contents
- [2020](#2020) (283)
- [2019](#2019) (261)
- [2018](#2018) (72)
- [2017](#2017) (21)
- [2016](#2016) (10)
- [1988~2015](#1988-2015) (12)
### Tasks
- [Medical](#Medical)
- [Image Segmentation](#Image_Segmentation) / [Object Detection](#Object_Detection) / [Semantic Segmentation](#Semantic_Segmentation) / [Image Recognition](#Image_Recognition)
- [Remote Sensing](#Remote_Sensing)
- [Scene Text Recognition](#Scene_Text_Recognition)
- [Autonomous Driving](#Autonomous_Driving)
- [Image Translator](#Image_Translator) / [Image Denoising](#Image_Denoising)
- [Language Modeling](#Language_Modeling) / [Speech Recognition](#Speech_Recognition) / [NLP](#NLP)
- [Model Compression](#Model_Compression) / [Multi-objective Search](#Multi-objective_Search) / [Binary Networks](#Binary_Networks)
- [GAN](#GAN) / [Unsupervised](#Unsupervised)
- [Video Models](#Video_Models)
- [GNN](#GNN)
- [CTR](#CTR)
- [Time Series](#Time_Series)
- [Federated Learning](#Federated_Learning) / [Private Inference](#Private_Inference)
- [3D Deep Learning](#3D_Deep_Learning)
- [Multimodal Learning](#Multimodal_Learning)
- [Imitation Learning](#Imitation_Learning)
- [Meta-learning](#Meta-learning)
- [Distributed System](#Distributed_System)
- [Benchmark](#Benchmark)
### Medical
- [back to top](#Tasks)
|Year | Title | Code |
|:--------|:--------|:--------:|
| 2020 | [Deep Convolution Features in Non-linear Embedding Space for Fundus Image Classification(Dondeti et al. 2020)](http://www.iieta.org/journals/ria/paper/10.18280/ria.340308)
*accepted at Revue d’Intelligence Artificielle* | |
| 2020 | [Searching Collaborative Agents for Multi-plane Localization in 3D Ultrasound(Huang et al. 2020)](https://arxiv.org/abs/2007.15273)
*accepted at MICCAI 2020* | |
| 2020 | [Multi-Modality Information Fusion for Radiomics-based Neural Architecture Search(Peng et al. 2020)](https://arxiv.org/abs/2007.06002)
*accepted at MICCAI 2020* | |
| 2020 | [Modeling Task-based fMRI Data via Deep Belief Network with Neural Architecture Search(Qiang et al. 2020)](https://www.sciencedirect.com/science/article/abs/pii/S0895611120300501)
*accepted at Computerized Medical Imaging and Graphics* | |
| 2020 | [AdaEn-Net: An Ensemble of Adaptive 2D-3D Fully Convolutional Networks for Medical Image Segmentation(Baldeon Calisto and Lai-Yuen. 2020)](https://www.sciencedirect.com/science/article/pii/S0893608020300848)
*accepted at Neural Networks* | |
| 2020 | [AutoSegNet: An Automated Neural Network for Image Segmentation(Xu et al. 2020)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9095283)
*accepted at IEEE Access* | |
| 2020 | [Optimize CNN Model for FMRI Signal Classification Via Adanet-Based Neural Architecture Search(Dai et al. 2020)](https://ieeexplore.ieee.org/abstract/document/9098574)
*accepted at IEEE ISBI* | |
| 2020 | [Neural Architecture Search for Skin Lesion Classification(Kwasigroch et al. 2020)](https://ieeexplore.ieee.org/document/8950333)
*accepted at IEEE Access* | |
| 2019 | [Scalable Neural Architecture Search for 3D Medical Image Segmentation(Kim et al. 2019)](https://arxiv.org/abs/1906.05956)
*accepted at MICCAI’19* | |
| 2019 | [Neural Architecture Search for Adversarial Medical Image Segmentation(Dong et al. 2019)](https://link.springer.com/chapter/10.1007/978-3-030-32226-7_92)
*accepted at MICCAI’19* | |
| 2019 | [Searching Learning Strategy with Reinforcement Learning for 3D Medical Image Segmentation(Yang et al. 2019)](https://link.springer.com/chapter/10.1007/978-3-030-32245-8_1)
*accepted at MICCAI’19* | |
| 2019 | [Resource Optimized Neural Architecture Search for 3D Medical Image Segmentation(Bae et al. 2019)](https://arxiv.org/abs/1909.00548)
*accepted at MICCAI’19* | |
| 2019 | [Self-Adaptive 2D-3D Ensemble of Fully Convolutional Networks for Medical Image Segmentation(Calisto and Lai-Yuen. 2019)](https://arxiv.org/abs/1907.11587)
*accepted at SPIE Medical Imaging’20* | |
| 2019 | [AdaResU-Net: Multiobjective Adaptive Convolutional Neural Network for Medical Image Segmentation(Baldeon-Calisto and Lai-Yuen. 2019.)](https://www.sciencedirect.com/science/article/pii/S0925231219304679)
*accepted at Neurocomputing* | |
| 2019 | [NAS-Unet: Neural Architecture Search for Medical Image Segmentation(Weng et al. 2019)](https://ieeexplore.ieee.org/document/8681706)
*accepted at IEEE Access* | |
| 2020 | [Efficient Oct Image Segmentation Using Neural Architecture Search(Gheshlaghi et al. 2020)](https://arxiv.org/abs/2007.14790) | |
| 2020 | [MS-NAS: Multi-Scale Neural Architecture Search for Medical Image Segmentation(Yan et al. 2020)](https://arxiv.org/abs/2007.06151) | |
| 2020 | [Heuristic Architecture Search Using Network Morphism for Chest X-Ray Classification(Radiuk and Kutucu 2020)](http://ceur-ws.org/Vol-2623/paper11.pdf) | |
| 2020 | [Evolving Deep Neural Networks for X-ray Based Detection of Dangerous Objects(Tsukada et al. 2020)](https://link.springer.com/chapter/10.1007/978-981-15-3685-4_12) | |
| 2020 | [Neural Architecture Search for Gliomas Segmentation on Multimodal Magnetic Resonance Imaging(Wang et al. 2020)](https://arxiv.org/abs/2005.06338) | |
| 2020 | [AutoHR: A Strong End-to-end Baseline for Remote Heart Rate Measurement with Neural Searching(Yu et al. 2020)](https://arxiv.org/abs/2004.12292) | |
| 2020 | [Organ at Risk Segmentation for Head and Neck Cancer using Stratified Learning and Neural Architecture Search(Guo et al. 2020)](https://arxiv.org/abs/2004.08426) | |
| 2020 | [ElixirNet: Relation-aware Network Architecture Adaptation for Medical Lesion Detection(Jiang et al. 2020)](https://arxiv.org/abs/2003.08770) | |
| 2020 | [Neural Architecture Search for Compressed Sensing Magnetic Resonance Image Reconstruction(Yan et al. 2020)](https://arxiv.org/abs/2002.09625) | [Github](https://github.com/yjump/NAS-for-CSMRI) |
| 2020 | [ENAS U-Net: Evolutionary Neural Architecture Search for Retinal Vessel(Fan et al. 2020)](https://arxiv.org/abs/2001.06678) | |
| 2019 | [C2FNAS: Coarse-to-Fine Neural Architecture Search for 3D Medical Image Segmentation(Yu et al. 2019)](https://arxiv.org/abs/1912.09628) | |
| 2019 | [SegNAS3D: Network Architecture Search with Derivative-Free Global Optimization for 3D Image Segmentation(Wong and Moradi. 2019)](https://arxiv.org/abs/1909.05962) | |
| 2019 | [V-NAS: Neural Architecture Search for Volumetric Medical Image Segmentation(Zhu et al. 2019)](https://arxiv.org/abs/1906.02817) | |
| 2019 | [Efficient Neural Architecture Search on Low-Dimensional Data for OCT Image Segmentation(Gessert and Schlaefer. 2019)](https://openreview.net/forum?id=Syg3FDjntN) | |
| 2018 | [Automatically Designing CNN Architectures for Medical Image Segmentation(Mortazi and Bagci 2018)](https://arxiv.org/abs/1807.07663) | |
### Image_Segmentation
- [back to top](#Tasks)
|Year | Title | Code |
|:--------|:--------|:--------:|
| 2020 | [AdaEn-Net: An Ensemble of Adaptive 2D-3D Fully Convolutional Networks for Medical Image Segmentation(Baldeon Calisto and Lai-Yuen. 2020)](https://www.sciencedirect.com/science/article/pii/S0893608020300848)
*accepted at Neural Networks* | |
| 2020 | [AutoSegNet: An Automated Neural Network for Image Segmentation(Xu et al. 2020)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9095283)
*accepted at IEEE Access* | |
| 2020 | [Fast Neural Network Adaptation via Parameter Remapping and Architecture Search(Fang et al. 2020)](https://arxiv.org/abs/2001.02525)
*accepted at ICLR’20* | [Github](https://github.com/JaminFong/FNA) |
| 2019 | [Scalable Neural Architecture Search for 3D Medical Image Segmentation(Kim et al. 2019)](https://arxiv.org/abs/1906.05956)
*accepted at MICCAI’19* | |
| 2019 | [Neural Architecture Search for Adversarial Medical Image Segmentation(Dong et al. 2019)](https://link.springer.com/chapter/10.1007/978-3-030-32226-7_92)
*accepted at MICCAI’19* | |
| 2019 | [Searching Learning Strategy with Reinforcement Learning for 3D Medical Image Segmentation(Yang et al. 2019)](https://link.springer.com/chapter/10.1007/978-3-030-32245-8_1)
*accepted at MICCAI’19* | |
| 2019 | [Resource Optimized Neural Architecture Search for 3D Medical Image Segmentation(Bae et al. 2019)](https://arxiv.org/abs/1909.00548)
*accepted at MICCAI’19* | |
| 2019 | [Self-Adaptive 2D-3D Ensemble of Fully Convolutional Networks for Medical Image Segmentation(Calisto and Lai-Yuen. 2019)](https://arxiv.org/abs/1907.11587)
*accepted at SPIE Medical Imaging’20* | |
| 2019 | [AdaResU-Net: Multiobjective Adaptive Convolutional Neural Network for Medical Image Segmentation(Baldeon-Calisto and Lai-Yuen. 2019.)](https://www.sciencedirect.com/science/article/pii/S0925231219304679)
*accepted at Neurocomputing* | |
| 2019 | [NAS-Unet: Neural Architecture Search for Medical Image Segmentation(Weng et al. 2019)](https://ieeexplore.ieee.org/document/8681706)
*accepted at IEEE Access* | |
| 2019 | [Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation(Liu et al. 2019)](https://arxiv.org/abs/1901.02985)
*accepted at CVPR’19* | [Github](https://github.com/MenghaoGuo/AutoDeeplab) |
| 2018 | [Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary Cells(Nekrasov et al. 2018)](https://arxiv.org/abs/1810.10804)
*accepted at CVPR’19* | |
| 2020 | [Efficient Oct Image Segmentation Using Neural Architecture Search(Gheshlaghi et al. 2020)](https://arxiv.org/abs/2007.14790) | |
| 2020 | [MS-NAS: Multi-Scale Neural Architecture Search for Medical Image Segmentation(Yan et al. 2020)](https://arxiv.org/abs/2007.06151) | |
| 2020 | [DCNAS: Densely Connected Neural Architecture Search for Semantic Image Segmentation(Zhang et al. 2020)](https://arxiv.org/abs/2003.11883) | |
| 2020 | [ENAS U-Net: Evolutionary Neural Architecture Search for Retinal Vessel(Fan et al. 2020)](https://arxiv.org/abs/2001.06678) | |
| 2019 | [C2FNAS: Coarse-to-Fine Neural Architecture Search for 3D Medical Image Segmentation(Yu et al. 2019)](https://arxiv.org/abs/1912.09628) | |
| 2019 | [SegNAS3D: Network Architecture Search with Derivative-Free Global Optimization for 3D Image Segmentation(Wong and Moradi. 2019)](https://arxiv.org/abs/1909.05962) | |
| 2019 | [Graph-guided Architecture Search for Real-time Semantic Segmentation(Lin et al. 2019)](https://arxiv.org/abs/1909.06793) | |
| 2019 | [SqueezeNAS: Fast neural architecture search for faster semantic segmentation(Shaw et al. 2019)](https://arxiv.org/abs/1908.01748) | |
| 2019 | [V-NAS: Neural Architecture Search for Volumetric Medical Image Segmentation(Zhu et al. 2019)](https://arxiv.org/abs/1906.02817) | |
| 2019 | [Efficient Neural Architecture Search on Low-Dimensional Data for OCT Image Segmentation(Gessert and Schlaefer. 2019)](https://openreview.net/forum?id=Syg3FDjntN) | |
| 2019 | [Template-Based Automatic Search of Compact Semantic Segmentation Architectures(Nekrasov et al. 2019)](https://arxiv.org/abs/1904.02365) | |
| 2018 | [Automatically Designing CNN Architectures for Medical Image Segmentation(Mortazi and Bagci 2018)](https://arxiv.org/abs/1807.07663) | |
### Model_Compression
- [back to top](#Tasks)
|Year | Title | Code |
|:--------|:--------|:--------:|
| 2020 | [Mixed-Precision Quantization for CNN-Based Remote Sensing Scene Classification(Wei et al. 2020)](https://ieeexplore.ieee.org/abstract/document/9153122)
*accepted at IEEE Geoscience and Remote Sensing Letters* | |
| 2020 | [Search What You Want: Barrier Panelty NAS for Mixed Precision Quantization(Yu et al. 2020)](https://arxiv.org/abs/2007.10026)
*accepted at ECCV 2020* | |
| 2020 | [Standing on the Shoulders of Giants: Hardware and Neural Architecture Co-Search with Hot Start(Jiang et al. 2020)](https://arxiv.org/abs/2007.09087)
*accepted at IEEE Transactions On Computer-Aided Design of Integrated Circuits and System* | |
| 2020 | [Finding Non-Uniform Quantization Schemes using Multi-Task Gaussian Processes(do Nascimento et al. 2020)](https://ui.adsabs.harvard.edu/abs/2020arXiv200707743G/abstract)
*accepted at ECCV 2020* | [Github](https://github.com/ActiveVisionLab/NUQ) |
| 2020 | [CP-NAS: Child-Parent Neural Architecture Search for 1-bit CNNs(Zhuo et al. 2020)](https://www.ijcai.org/Proceedings/2020/0144.pdf)
*accepted at IJCAI 2020* | |
| 2020 | [Butterfly Transform: An Efficient FFT Based Neural Architecture Design(Alizadeh vahid et al. 2020)](http://openaccess.thecvf.com/content_CVPR_2020/html/vahid_Butterfly_Transform_An_Efficient_FFT_Based_Neural_Architecture_Design_CVPR_2020_paper.html)
*accepted at CVPR 2020* | [Github](https://github.com/keivanalizadeh/ButterflyTransform) |
| 2020 | [NeuralScale: Efficient Scaling of Neurons for Resource-Constrained Deep Neural Networks(Lee and Lee)](http://openaccess.thecvf.com/content_CVPR_2020/papers/Lee_NeuralScale_Efficient_Scaling_of_Neurons_for_Resource-Constrained_Deep_Neural_Networks_CVPR_2020_paper.pdf)
*accepted at CVPR 2020* | [Github](https://github.com/eugenelet/NeuralScale) |
| 2020 | [Auto-Fas: Searching Lightweight Networks for Face Anti-Spoofing(Yu et al. 2020)](https://ieeexplore.ieee.org/abstract/document/9053587)
*accepted at accetped at ICASSP 2020* | |
| 2020 | [Accelerator-Aware Neural Network Design Using AutoML(Gupta and Akin. 2020)](https://arxiv.org/abs/2003.02838)
*accepted at On-device Intelligence Workshop at MLSys’20* | |
| 2020 | [Efficient Evolutionary Architecture Search for CNN Optimization on GTSRB(Johner and Wassner. 2020)](https://ieeexplore.ieee.org/abstract/document/8999305/)
*accepted at ICMLA’19* | |
| 2020 | [Automating Deep Neural Network Model Selection for Edge Inference(Lu et al. 2020)](https://ieeexplore.ieee.org/abstract/document/8998995)
*accepted at CogMI’20* | |
| 2020 | [Best of Both Worlds: AutoML Codesign of a CNN and its Model Compression(Abdelfattah et al. 2020)](https://arxiv.org/abs/2002.05022)
*accepted at DAC’20* | |
| 2020 | [Co-Exploration of Neural Architectures and Heterogeneous ASIC Accelerator Designs Targeting Multiple Tasks(Yang et al. 2020)](https://arxiv.org/abs/2002.04116)
*accepted at DAC’20* | |
| 2020 | [FPNet: Customized Convolutional Neural Network for FPGA Platforms(Yang et al. 2020)](https://ieeexplore.ieee.org/abstract/document/8977837)
*accepted at FPT’20* | |
| 2020 | [Search for Better Students to Learn Distilled Knowledge(Gu et al. 2020)](https://arxiv.org/abs/2001.11612)
*accepted at ECAI'20* | |
| 2020 | [HNAS: Hierarchical Neural Architecture Search on Mobile Devices(Xia et al. 2020)](https://arxiv.org/abs/2005.07564) | |
| 2020 | [Binarizing MobileNet via Evolution-based Searching(Phan et al. 2020)](https://arxiv.org/abs/2005.06305) | |
| 2020 | [CP-NAS: Child-Parent Neural Architecture Search for 1-bit CNNs( Zhuo et al. 2020)](https://arxiv.org/abs/2005.00057) | |
| 2020 | [MobileDets: Searching for Object Detection Architectures for Mobile Accelerators( Xiong et al. 2020)](https://arxiv.org/abs/2004.14525) | |
| 2020 | [GAN Compression: Efficient Architectures for Interactive Conditional GAN(Li et al. 2020)](https://arxiv.org/abs/2003.08936) | [Github](https://github.com/mit-han-lab/gan-compression) |
| 2020 | [Search for Winograd-Aware Quantized Networks(Fernandez-Marques et al. 2020)](https://arxiv.org/abs/2002.10711) | |
| 2020 | [AdaBERT: Task-Adaptive BERT Compression with Differentiable Neural Architecture Search(Chen et al. 2020)](https://arxiv.org/abs/2001.04246) | |
### Multi-objective_Search
- [back to top](#Tasks)
|Year | Title | Code |
|:--------|:--------|:--------:|
| 2020 | [CurveLane-NAS: Unifying Lane-Sensitive Architecture Search and Adaptive Point Blending(Xu et al. 2020)](https://arxiv.org/abs/2007.12147)
*accepted at ECCV 2020* | [Github](http://www.noahlab.com.hk/opensource/vega/#curvelanes) |
| 2020 | [NSGANetV2: Evolutionary Multi-Objective Surrogate-Assisted Neural Architecture Search(Lu et al. 2020)](https://arxiv.org/abs/2007.10396)
*accepted at ECCV 2020* | [Github](https://github.com/mikelzc1990/nsganetv2) |
| 2020 | [Neural-Architecture-Search-Based Multiobjective Cognitive Automation System(Wang et al. 2020)](https://ieeexplore.ieee.org/abstract/document/9127493)
*accepted at IEEE System Journal* | |
| 2020 | [Beyond Network Pruning: a Joint Search-and-Training Approach(Lu et al. 2020)](http://see.xidian.edu.cn/faculty/wsdong/Papers/Conference/ijcai20.pdf)
*accepted at IJCAI 2020* | |
| 2020 | [Hardware-Aware Transformable Architecture Search with Efficient Search Space(Jiang et al. 2020)](https://ieeexplore.ieee.org/abstract/document/9102721)
*accepted at accpeted at ICME 2020* | |
| 2020 | [Fast Hardware-Aware Neural Architecture Search(Zhang et al. 2020)](http://openaccess.thecvf.com/content_CVPRW_2020/html/w40/Zhang_Fast_Hardware-Aware_Neural_Architecture_Search_CVPRW_2020_paper.html)
*accepted at CVPR 2020 workshop* | |
| 2020 | [MemNAS: Memory-Efficient Neural Architecture Search with Grow-Trim Learning(Liu et al.2020)](http://openaccess.thecvf.com/content_CVPR_2020/html/Liu_MemNAS_Memory-Efficient_Neural_Architecture_Search_With_Grow-Trim_Learning_CVPR_2020_paper.html)
*accepted at CVPR 2020* | [Github](https://github.com/MemNAS/MemNAS) |
| 2020 | [APQ: Joint Search for Network Architecture, Pruning and Quantization Policy(Wang et al.2020)](https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_APQ_Joint_Search_for_Network_Architecture_Pruning_and_Quantization_Policy_CVPR_2020_paper.html)
*accepted at CVPR 2020* | [Github](https://github.com/mit-han-lab/apq) |
| 2020 | [Designing Resource-Constrained Neural Networks Using Neural Architecture Search Targeting Embedded Devices(Cassimon et al. 2020)](https://www.sciencedirect.com/science/article/pii/S2542660520300676)
*accepted at IEEE Internet of Things* | |
| 2020 | [FTT-NAS: Discovering Fault-Tolerant Neural Architecture(Li et al. 2020)](http://nicsefc.ee.tsinghua.edu.cn/media/publications/2020/ASPDAC20_293_6p4Ghq4.pdf)
*accepted at ASP-DAC 2020* | |
| 2020 | [DeepMaker: A multi-objective optimization framework for deep neural networks in embedded systems(Loni et al. 2020)](https://www.sciencedirect.com/science/article/abs/pii/S0141933119301176)
*accepted at Microprocessors and Microsystems* | |
| 2020 | [Multi-Objective Neural Architecture Search Based on Diverse Structures and Adaptive Recommendation(Wang et al. 2020)](https://arxiv.org/abs/2007.02749) | [Github](https://github.com/wangcn0201/MoARR) |
| 2020 | [You Only Search Once: A Fast Automation Framework for Single-Stage DNN/Accelerator Co-design(Chen et al. 2020)](https://arxiv.org/abs/2005.07075) | |
| 2020 | [FlexiBO: Cost-Aware Multi-Objective Optimization of Deep Neural Networks(Iqbal et al. 2020)](https://arxiv.org/abs/2001.06588) | [Github](https://github.com/softsys4ai/FlexiBO) |
### Object_Detection
- [back to top](#Tasks)
|Year | Title | Code |
|:--------|:--------|:--------:|
| 2020 | [Representation Sharing for Fast Object Detector Search and Beyond(Zhou et al .2020)](https://arxiv.org/abs/2007.12075)
*accepted at ECCV 2020* | |
| 2020 | [FNA++: Fast Network Adaptation via Parameter Remapping and Architecture Search(Fang et al. 2020)](https://arxiv.org/abs/2006.12986)
*accepted at ICLR 2020* | [Github](https://github.com/JaminFong/FNA) |
| 2020 | [SP-NAS: Serial-to-Parallel Backbone Search for Object Detection(Jiang et al. 2020)](http://openaccess.thecvf.com/content_CVPR_2020/html/Jiang_SP-NAS_Serial-to-Parallel_Backbone_Search_for_Object_Detection_CVPR_2020_paper.html)
*accepted at CVPR 2020* | |
| 2020 | [Automated Design of Neural Network Architectures with Reinforcement Learning for Detection of Global Manipulations(Chen et al. 2020)](https://ieeexplore.ieee.org/abstract/document/9103245)
*accepted at IEEE Journal of Selected Topics in Signal Processing* | |
| 2020 | [Hit-Detector: Hierarchical Trinity Architecture Search for Object Detection(Guo et al. 2020)](https://arxiv.org/abs/2003.11818)
*accepted at CVPR 2020* | [Github](https://github.com/ggjy/HitDet.pytorch) |
| 2020 | [Fast Neural Network Adaptation via Parameter Remapping and Architecture Search(Fang et al. 2020)](https://arxiv.org/abs/2001.02525)
*accepted at ICLR’20* | [Github](https://github.com/JaminFong/FNA) |
| 2019 | [Auto-FPN: Automatic Network Architecture Adaptation for Object Detection Beyond Classification(Xu et al. 2019)](https://openaccess.thecvf.com/content_ICCV_2019/html/Xu_Auto-FPN_Automatic_Network_Architecture_Adaptation_for_Object_Detection_Beyond_Classification_ICCV_2019_paper.html)
*accepted at ICCV’19* | |
| 2019 | [DetNAS: Neural Architecture Search on Object Detection(Chen et al. 2019)](https://arxiv.org/abs/1903.10979)
*accepted at NeurIPS’19* | [Github](https://github.com/megvii-model/DetNAS) |
| 2020 | [MobileDets: Searching for Object Detection Architectures for Mobile Accelerators( Xiong et al. 2020)](https://arxiv.org/abs/2004.14525) | |
### GAN
- [back to top](#Tasks)
|Year | Title | Code |
|:--------|:--------|:--------:|
| 2020 | [Off-Policy Reinforcement Learning for Efficient and Effective GAN Architecture Search(Tian et al. 2020)](https://arxiv.org/abs/2007.09180)
*accepted at ECCV 2020* | [Github](https://github.com/Yuantian013/E2GAN) |
| 2020 | [A Multi-objective architecture search for generative adversarial networks(Kobayashi et al. 2020)](https://dl.acm.org/doi/abs/10.1145/3377929.3390004)
*accepted at GECCO 2020* | |
| 2020 | [AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks(Fu et al. 2020)](https://arxiv.org/abs/2006.08198)
*accepted at ICML 2020* | [Github](https://github.com/VITA-Group/AGD) |
| 2019 | [AutoGAN: Neural Architecture Search for Generative Adversarial Networks(Gong et al. 2019)](https://arxiv.org/abs/1908.03835)
*accepted at ICCV’19* | [Github](https://github.com/VITA-Group/AutoGAN) |
| 2020 | [Searching towards Class-Aware Generators for Conditional Generative Adversarial Networks(Zhou et al. 2020)](https://arxiv.org/abs/2006.14208) | [Github](https://github.com/PeterouZh/NAS_cGAN) |
| 2020 | [AlphaGAN: Fully Differentiable Architecture Search for Generative Adversarial Networks(Tian et al. 2020)](https://arxiv.org/abs/2006.09134) | [Github](https://github.com/yuesongtian/AlphaGAN) |
| 2020 | [Conditional Neural Architecture Search(Kao et al. 2020)](https://arxiv.org/abs/2006.03969) | |
| 2020 | [GAN Compression: Efficient Architectures for Interactive Conditional GAN(Li et al. 2020)](https://arxiv.org/abs/2003.08936) | [Github](https://github.com/mit-han-lab/gan-compression) |
### Image_Translator
- [back to top](#Tasks)
|Year | Title | Code |
|:--------|:--------|:--------:|
| 2020 | [AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks(Fu et al. 2020)](https://arxiv.org/abs/2006.08198)
*accepted at ICML 2020* | [Github](https://github.com/VITA-Group/AGD) |
| 2020 | [Memory-Efficient Hierarchical Neural Architecture Search for Image Denoising(Zhang et al. 2020)](http://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Memory-Efficient_Hierarchical_Neural_Architecture_Search_for_Image_Denoising_CVPR_2020_paper.html)
*accepted at CVPR 2020* | |
| 2020 | [All in One Bad Weather Removal using Architectural Search(Li et al. 2020)](https://openaccess.thecvf.com/content_CVPR_2020/html/Li_All_in_One_Bad_Weather_Removal_Using_Architectural_Search_CVPR_2020_paper.html)
*accepted at CVPR 2020* | |
| 2020 | [Journey Towards Tiny Perceptual Super-Resolution(Lee et al. 2020)](https://arxiv.org/abs/2007.04356) | |
| 2020 | [Hierarchical Neural Architecture Search for Single Image Super-Resolution(Guo et al. 2020)](https://arxiv.org/abs/2003.04619) | |
| 2020 | [Automatically Searching for U-Net Image Translator Architecture(Shu and Wang. 2020)](https://arxiv.org/abs/2002.11581) | |
### Video_Models
- [back to top](#Tasks)
|Year | Title | Code |
|:--------|:--------|:--------:|
| 2020 | [AttentionNAS: Spatiotemporal Attention Cell Search for Video Classification(Wang et al. 2020)](https://arxiv.org/abs/2007.12034)
*accepted at ECCV 2020* | |
| 2020 | [Architecture Search of Dynamic Cells for Semantic Video Segmentation(Nekrasov et al. 2020)](https://openaccess.thecvf.com/content_WACV_2020/html/Nekrasov_Architecture_Search_of_Dynamic_Cells_for_Semantic_Video_Segmentation_WACV_2020_paper.html)
*accepted at WACV 2020* | |
| 2020 | [Tiny Video Networks: Architecture Search for Efficient Video Models(Piergiovanni et al. 2020)](https://research.google/pubs/pub49263/)
*accepted at 7th ICML Workshop on Automated Machine Learning, 2020* | |
| 2018 | [Evolving Space-Time Neural Architectures for Videos(Piergiovanni et al. 2018)](https://arxiv.org/abs/1811.10636)
*accepted at ICCV’19* | |
| 2019 | [Video Action Recognition via Neural Architecture Searching(Peng et al. 2019)](https://arxiv.org/abs/1907.04632) | |
| 2019 | [AssembleNet: Searching for Multi-Stream Neural Connectivity in Video Architectures(Ryoo et al. 2019)](https://arxiv.org/abs/1905.13209) | |
### GNN
- [back to top](#Tasks)
|Year | Title | Code |
|:--------|:--------|:--------:|
| 2020 | [Graph Neural Architecture Search(Gao et al. 2020)](https://www.researchgate.net/profile/Chuan_Zhou5/publication/342789484_Graph_Neural_Architecture_Search/links/5f0be495299bf18816197d15/Graph-Neural-Architecture-Search.pdf)
*accepted at IJCAI 2020* | [Github](https://github.com/GraphNAS/GraphNAS) |
| 2020 | [A Semi-Supervised Assessor of Neural Architectures(Tang et al. 2020)](https://arxiv.org/abs/2005.06821)
*accepted at CVPR 2020* | |
| 2020 | [Neural Architecture Optimization with Graph VAE(Li et al. 2020)](https://arxiv.org/abs/2006.10310) | |
| 2020 | [A Generic Graph-based Neural Architecture Encoding Scheme for Predictor-based NAS(Ning et al. 2020)](https://arxiv.org/abs/2004.01899)
*accepted at ECCV 2020* | [Github](https://github.com/walkerning/aw_nas) |
| 2020 | [Probabilistic Dual Network Architecture Search on Graphs(Zhao et al. 2020)](https://arxiv.org/abs/2003.09676) | |
### Unsupervised
- [back to top](#Tasks)
|Year | Title | Code |
|:--------|:--------|:--------:|
| 2020 | [Superkernel Neural Architecture Search for Image Denoising(Mozejko et al. 2020)](https://arxiv.org/abs/2004.08870)
*accepted at NTIRE2020 Workshop at CVPR 2020* | |
| 2020 | [An Evolutionary Approach to Variational Autoencoders(Hajewski and Oliveira. 2020)](https://ieeexplore.ieee.org/abstract/document/9031239)
*accepted at CCWC’20* | |
| 2020 | [Does Unsupervised Architecture Representation Learning Help Neural Architecture Search?(Yan et al. 2020)](https://arxiv.org/abs/2006.06936) | |
| 2020 | [Are Labels Necessary for Neural Architecture Search?(Liu et al. 2020)](https://arxiv.org/abs/2003.12056) | [Github](https://github.com/facebookresearch/unnas) |
### Binary_Networks
- [back to top](#Tasks)
|Year | Title | Code |
|:--------|:--------|:--------:|
| 2020 | [CP-NAS: Child-Parent Neural Architecture Search for 1-bit CNNs(Zhuo et al. 2020)](https://www.ijcai.org/Proceedings/2020/0144.pdf)
*accepted at IJCAI 2020* | |
| 2020 | [DMS: Differentiable Dimension Search for Binary Neural Networks(Li et al. 2020)](https://xhplus.github.io/publication/conference-paper/iclr2020/dms/DMS.pdf)
*accepted at 1st Workshop on Neural Architecture Search at ICLR 2020* | |
| 2020 | [BATS: Binary ArchitecTure Search(Bulat et al. 2020)](https://arxiv.org/abs/2003.01711)
*accepted at ECCV’20* | [Github](https://github.com/1adrianb/binary-nas) |
| 2020 | [Learning Architectures for Binary Networks(Kim et al. 2020)](https://arxiv.org/abs/2002.06963)
*accepted at ECCV’20* | |
### CTR
- [back to top](#Tasks)
|Year | Title | Code |
|:--------|:--------|:--------:|
| 2020 | [Towards Automated Neural Interaction Discovery for Click-Through Rate Prediction(Song et al. 2020)](https://arxiv.org/abs/2007.06434)
*accepted at KDD2020* | |
| 2020 | [AMER: Automatic Behavior Modeling and Interaction Exploration in Recommender System(Zhao et al. 2020)](https://arxiv.org/abs/2006.05933) | |
| 2020 | [Differentiable Neural Input Search for Recommender Systems(Cheng et al. 2020)](https://arxiv.org/abs/2006.04466) | |
| 2020 | [AutoEmb: Automated Embedding Dimensionality Search in Streaming Recommendations(Zhao et al. 2020)](https://arxiv.org/abs/2002.11252) | |
### Multimodal_Learning
- [back to top](#Tasks)
|Year | Title | Code |
|:--------|:--------|:--------:|
| 2020 | [Multi-Modality Information Fusion for Radiomics-based Neural Architecture Search(Peng et al. 2020)](https://arxiv.org/abs/2007.06002)
*accepted at MICCAI 2020* | |
| 2020 | [RandomNet: Towards Fully Automatic Neural Architecture Design for Multimodal Learning(Alletto et al. 2020)](https://arxiv.org/abs/2003.01181)
*accepted at Meta-Eval 2020 workshop* | |
| 2019 | [MFAS: Multimodal Fusion Architecture Search(Pérez-Rúa et al. 2019)](https://hal.archives-ouvertes.fr/hal-02068293/document)
*accepted at CVPR’19* | |
| 2020 | [Deep Multimodal Neural Architecture Search(Yu et al. 2020)](https://arxiv.org/abs/2004.12070) | |
### Federated_Learning
- [back to top](#Tasks)
|Year | Title | Code |
|:--------|:--------|:--------:|
| 2020 | [FedNAS: Federated Deep Learning via Neural Architecture Search(He et al. 2020)](https://chaoyanghe.com/publications/FedNAS-CVPR2020-NAS.pdf)
*accepted at CVPR 2020 Workshop on Neural Architecture Search and Beyond for Representation Learning* | [Github](https://github.com/chaoyanghe/FedNAS) |
| 2020 | [Differentially-private Federated Neural Architecture Search(Singh et al. 2020)](https://arxiv.org/abs/2006.10559) | [Github](https://github.com/UCSD-AI4H/DP-FNAS) |
| 2020 | [Real-time Federated Evolutionary Neural Architecture Search(Zhu and Jin. 2020)](https://arxiv.org/abs/2003.02793) | |
| 2020 | [Neural Architecture Search over Decentralized Data(Xu et al. 2020)](https://arxiv.org/abs/2002.06352) | |
### Speech_Recognition
- [back to top](#Tasks)
|Year | Title | Code |
|:--------|:--------|:--------:|
| 2020 | [DARTS-ASR: Differentiable Architecture Search for Multilingual Speech Recognition and Adaptation(Chen et al. 2020)](https://arxiv.org/abs/2005.07029)
*accepted at INTERSPEECH 2020* | |
| 2020 | [Neural Architecture Search for Speech Recognition(Hu et al. 2020)](https://arxiv.org/abs/2007.08818) | |
| 2020 | [AutoSpeech: Neural Architecture Search for Speaker Recognition(Ding et al. 2020)](https://arxiv.org/abs/2005.03215) | [Github](https://github.com/VITA-Group/AutoSpeech) |
### Benchmark
- [back to top](#Tasks)
|Year | Title | Code |
|:--------|:--------|:--------:|
| 2020 | [NAS-Bench-1Shot1: Benchmarking and Dissecting One-Shot Neural Architecture Search(Zela et al. 2020)](https://arxiv.org/abs/2001.10422)
*accepted at ICLR’20* | |
| 2020 | [NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search(Dong and Yang et al. 2020)](https://arxiv.org/abs/2001.00326)
*accepted at ICLR’20* | [Github](https://github.com/D-X-Y/AutoDL-Projects) |
| 2020 | [NAS-Bench-NLP: Neural Architecture Search Benchmark for Natural Language Processing(Klyuchnikov et al. 2020)](https://arxiv.org/abs/2006.07116) | |
### Remote_Sensing
- [back to top](#Tasks)
|Year | Title | Code |
|:--------|:--------|:--------:|
| 2020 | [Mixed-Precision Quantization for CNN-Based Remote Sensing Scene Classification(Wei et al. 2020)](https://ieeexplore.ieee.org/abstract/document/9153122)
*accepted at IEEE Geoscience and Remote Sensing Letters* | |
| 2020 | [RSNet: The Search for Remote Sensing Deep Neural Networks in Recognition Tasks(Wang et al. 2020)](https://ieeexplore.ieee.org/abstract/document/9123590)
*accepted at IEEE Transactions on Geoscience and Remote Sensing* | |
| 2020 | [Convolution Neural Network Architecture Learning for Remote Sensing Scene Classification(Chen et al. 2010)](https://arxiv.org/abs/2001.09614) | |
### 3D_Deep_Learning
- [back to top](#Tasks)
|Year | Title | Code |
|:--------|:--------|:--------:|
| 2020 | [Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution(Tang et al. 2020)](https://arxiv.org/abs/2007.16100)
*accepted at ECCV 2020* | |
| 2020 | [Lidar Data Classification Based on Automatic Designed CNN(Xie and Chen 2020)](https://ieeexplore.ieee.org/abstract/document/9139215)
*accepted at IEEE Geoscience and Remote Sensing Letters* | |
| 2020 | [Fusion Mechanisms for Human Activity Recognition using Automated Machine Learning(Popescu et al. 2020)](https://ieeexplore.ieee.org/document/9153764)
*accepted at IEEE Access* | |
### Scene_Text_Recognition
- [back to top](#Tasks)
|Year | Title | Code |
|:--------|:--------|:--------:|
| 2020 | [Memory-Efficient Models for Scene Text Recognition via Neural Architecture Search(Hong et al. 2020)](https://openaccess.thecvf.com/content_WACVW_2020/html/w3/Hong_Memory-Efficient_Models_for_Scene_Text_Recognition_via_Neural_Architecture_Search_WACVW_2020_paper.html)
*accepted at WACV’20 workshop* | |
| 2020 | [Efficient Backbone Search for Scene Text Recognition(Zhang et al. 2020)](https://arxiv.org/abs/2003.06567) | |
### NLP
- [back to top](#Tasks)
|Year | Title | Code |
|:--------|:--------|:--------:|
| 2020 | [NAS-Bench-NLP: Neural Architecture Search Benchmark for Natural Language Processing(Klyuchnikov et al. 2020)](https://arxiv.org/abs/2006.07116) | |
| 2020 | [AdaBERT: Task-Adaptive BERT Compression with Differentiable Neural Architecture Search(Chen et al. 2020)](https://arxiv.org/abs/2001.04246) | |
### Private_Inference
- [back to top](#Tasks)
|Year | Title | Code |
|:--------|:--------|:--------:|
| 2020 | [SOTERIA: In Search of Efficient Neural Networks for Private Inference(Aggarwal et al. 2020)](https://arxiv.org/abs/2007.12934) | |
| 2020 | [CryptoNAS: Private Inference on a ReLU Budget(Ghodsi et al. 2020)](https://arxiv.org/abs/2006.08733) | |
### Imitation_Learning
- [back to top](#Tasks)
|Year | Title | Code |
|:--------|:--------|:--------:|
| 2020 | [NASIL: Neural Architecture Search With Imitation Learning(Fard et al. 2020)](https://ieeexplore.ieee.org/document/9054748)
*accepted at ICASSP 2020* | |
| 2020 | [AutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learning(Li et al. 2020)](https://arxiv.org/abs/2006.11321) | |
### Time_Series
- [back to top](#Tasks)
|Year | Title | Code |
|:--------|:--------|:--------:|
| 2020 | [Neural Architecture Search for Time Series Classification(Rakhshani et al. 2020)](https://germain-forestier.info/publis/ijcnn2020.pdf)
*accepted at ijcnn 2020* | |
| 2020 | [Improving Neuroevolution Using Island Extinction And Repopulation(Lyu et al. 2020)](https://arxiv.org/abs/2005.07376) | |
### Semantic_Segmentation
- [back to top](#Tasks)
|Year | Title | Code |
|:--------|:--------|:--------:|
| 2020 | [Architecture Search of Dynamic Cells for Semantic Video Segmentation(Nekrasov et al. 2020)](https://openaccess.thecvf.com/content_WACV_2020/html/Nekrasov_Architecture_Search_of_Dynamic_Cells_for_Semantic_Video_Segmentation_WACV_2020_paper.html)
*accepted at WACV 2020* | |
| 2020 | [FNA++: Fast Network Adaptation via Parameter Remapping and Architecture Search(Fang et al. 2020)](https://arxiv.org/abs/2006.12986)
*accepted at ICLR 2020* | [Github](https://github.com/JaminFong/FNA) |
### Distributed_System
- [back to top](#Tasks)
|Year | Title | Code |
|:--------|:--------|:--------:|
| 2020 | [A Scalable System for Neural Architecture Search(Hajewski and Oliveira. 2020)](https://ieeexplore.ieee.org/abstract/document/9031181)
*accepted at CCWC’20* | |
| 2020 | [Distributed Evolution of Deep Autoencoders(Hajewski et al. 2020)](https://arxiv.org/abs/2004.07607) | |
### Autonomous_Driving
- [back to top](#Tasks)
|Year | Title | Code |
|:--------|:--------|:--------:|
| 2020 | [Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution(Tang et al. 2020)](https://arxiv.org/abs/2007.16100)
*accepted at ECCV 2020* | |
| 2020 | [CurveLane-NAS: Unifying Lane-Sensitive Architecture Search and Adaptive Point Blending(Xu et al. 2020)](https://arxiv.org/abs/2007.12147)
*accepted at ECCV 2020* | [Github](http://www.noahlab.com.hk/opensource/vega/#curvelanes) |
### Meta-learning
- [back to top](#Tasks)
|Year | Title | Code |
|:--------|:--------|:--------:|
| 2020 | [CATCH: Context-based Meta Reinforcement Learning for Transferrable Architecture Search(Chen et al. 2020)](https://arxiv.org/abs/2007.09380)
*accepted at ECCV 2020* | |
| 2020 | [M-NAS: Meta Neural Architecture Search(Wang et al. 2020)](https://aaai.org/ojs/index.php/AAAI/article/view/6084)
*accepted at AAAI 2020* | |
### Language_Modeling
- [back to top](#Tasks)
|Year | Title | Code |
|:--------|:--------|:--------:|
| 2020 | [Searching Better Architectures for Neural Machine Translation(Fan et al. 2020)](https://ieeexplore.ieee.org/abstract/document/9095246)
*accepted at IEEE/ACM Transactions on Audio, Speech, and Language Processing* | |
| 2020 | [Learning Architectures from an Extended Search Space for Language Modeling(Li et al. 2020)](https://arxiv.org/abs/2005.02593)
*accepted at ACL 2020* | |
### Image_Denoising
- [back to top](#Tasks)
|Year | Title | Code |
|:--------|:--------|:--------:|
| 2020 | [Superkernel Neural Architecture Search for Image Denoising(Mozejko et al. 2020)](https://arxiv.org/abs/2004.08870)
*accepted at NTIRE2020 Workshop at CVPR 2020* | |
| 2020 | [Neural Architecture Search for Deep Image Prior(Ho et al. 2020)](https://arxiv.org/abs/2001.04776) | |
### Image_Recognition
- [back to top](#Tasks)
|Year | Title | Code |
|:--------|:--------|:--------:|
| 2020 | [On Network Design Spaces for Visual Recognition(Radosavovic et al. 2020)](https://openaccess.thecvf.com/content_ICCV_2019/html/Radosavovic_On_Network_Design_Spaces_for_Visual_Recognition_ICCV_2019_paper.html)
*accepted at ICCV 2019* | [Github](https://github.com/facebookresearch/pycls) |
| 2020 | [Memory-Efficient Models for Scene Text Recognition via Neural Architecture Search(Hong et al. 2020)](https://openaccess.thecvf.com/content_WACVW_2020/html/w3/Hong_Memory-Efficient_Models_for_Scene_Text_Recognition_via_Neural_Architecture_Search_WACVW_2020_paper.html)
*accepted at WACV’20 workshop* | |
****
### 2020
| Title | Tags | Code |
|:--------|:--------:|:--------:|
| [Deep Convolution Features in Non-linear Embedding Space for Fundus Image Classification(Dondeti et al. 2020) ](http://www.iieta.org/journals/ria/paper/10.18280/ria.340308)
*accepted at Revue d’Intelligence Artificielle* | Medical
Image Classification
NASNet | - |
| [A Unified Approach to Anomaly Detection(Ball et al. 2020) ](https://www.researchgate.net/publication/343006753_A_Unified_Approach_to_Anomaly_Detection)
*accepted at The Sixth International Conference on Machine Learning* | Anomaly Detection
AutoEncoder
Evoluationary | - |
| [Evolving Multi-Resolution Pooling CNN for Monaural Singing Voice Separation(Yuan et al. 2020) ](https://arxiv.org/abs/2008.00816) | Monaural Singing Voice Separation
Evolutionary | - |
| [Weight-Sharing Neural Architecture Search: A Battle to Shrink the Optimization Gap(Xie et al. 2020) ](https://arxiv.org/abs/2008.01475) | Survey
CV | - |
| [Neural Architecture Search in Graph Neural Networks(Nunes and L.Pappa 2020) ](https://arxiv.org/abs/2008.00077) | Graph Neural Networks
Evolutionary
RL| - |
| [Anti-Bandit Neural Architecture Search for Model Defense(Chen et al. 2020) ](https://arxiv.org/abs/2008.00698)
*accepted at ECCV 2020* | Adversarial Defense
ABanditNAS | [Github](https://github.com/bczhangbczhang/ABanditNAS) |
| [HMCNAS: Neural Architecture Search Using Hidden Markov Chains And Bayesian Optimization(Lopes and Alexandre 2020) ](https://arxiv.org/abs/2007.16149) | HMCNAS
Evolutionary | - |
| [Neural Architecture Search as Sparse Supernet(Wu et al. 2020) ](https://arxiv.org/abs/2007.16112) | - | - |
| [Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution(Tang et al. 2020) ](https://arxiv.org/abs/2007.16100)
*accepted at ECCV 2020* | 3D Deep Learning
Autonomous Driving
Resource Constraints
Evolutionary | - |
| [Growing Efficient Deep Networks by Structured Continuous Sparsification(Yuan et al. 2020) ](https://arxiv.org/abs/2007.15353) | Network Pruning | - |
| [Lidar Data Classification Based on Automatic Designed CNN(Xie and Chen 2020) ](https://ieeexplore.ieee.org/abstract/document/9139215)
*accepted at IEEE Geoscience and Remote Sensing Letters* | 3D Deep Learning
Gradient-based | - |
| [Fusion Mechanisms for Human Activity Recognition using Automated Machine Learning(Popescu et al. 2020) ](https://ieeexplore.ieee.org/document/9153764)
*accepted at IEEE Access* | Human Activity Recognition
3D Deep Learning
CV
RL | - |
| [Mixed-Precision Quantization for CNN-Based Remote Sensing Scene Classification(Wei et al. 2020) ](https://ieeexplore.ieee.org/abstract/document/9153122)
*accepted at IEEE Geoscience and Remote Sensing Letters* | Remote Sensing
Model Compression
Mixed-Precision Quantization | - |
| [Searching Collaborative Agents for Multi-plane Localization in 3D Ultrasound(Huang et al. 2020) ](https://arxiv.org/abs/2007.15273)
*accepted at MICCAI 2020* | Medical
GDAS
RL | - |
| [TF-NAS: Rethinking Three Search Freedoms of Latency-Constrained Differentiable Neural Architecture Search(Hu et al. 2020) ](https://arxiv.org/abs/2008.05314)
*accepted at ECCV 2020* | TF-NAS | [Github](https://github.com/AberHu/TF-NAS) |
| [Efficient Oct Image Segmentation Using Neural Architecture Search(Gheshlaghi et al. 2020) ](https://arxiv.org/abs/2007.14790) | Medical
Image Segmentation
ProxylessNAS | - |
| [SOTERIA: In Search of Efficient Neural Networks for Private Inference(Aggarwal et al. 2020) ](https://arxiv.org/abs/2007.12934) | Private Inference
DARTS | - |
| [What and Where: Learn to Plug Adapters via NAS for Multi-Domain Learning(Zhao et al. 2020) ](https://arxiv.org/abs/2007.12415) | Multi-Domain Learning | - |
| [CurveLane-NAS: Unifying Lane-Sensitive Architecture Search and Adaptive Point Blending(Xu et al. 2020) ](https://arxiv.org/abs/2007.12147)
*accepted at ECCV 2020* | Autonomous Driving
Lane Detection
Multi-objective Search
Evolutionary | [Dataset](http://www.noahlab.com.hk/opensource/vega/#curvelanes) |
| [Representation Sharing for Fast Object Detector Search and Beyond(Zhou et al .2020) ](https://arxiv.org/abs/2007.12075)
*accepted at ECCV 2020* | Object Detection
| - |
| [AttentionNAS: Spatiotemporal Attention Cell Search for Video Classification(Wang et al. 2020) ](https://arxiv.org/abs/2007.12034)
*accepted at ECCV 2020* | Video Models
DARTS | - |
| [MCUNet: Tiny Deep Learning on IoT Devices(Lin et al. 2020) ](https://arxiv.org/abs/2007.10319) | IoT
| - |
| [Search What You Want: Barrier Panelty NAS for Mixed Precision Quantization(Yu et al. 2020) ](https://arxiv.org/abs/2007.10026)
*accepted at ECCV 2020* | Model Compression
Mixed Precision Quantization
DARTS | - |
| [NSGANetV2: Evolutionary Multi-Objective Surrogate-Assisted Neural Architecture Search(Lu et al. 2020) ](https://arxiv.org/abs/2007.10396)
*accepted at ECCV 2020* | Multi-objective Search
| [Github](https://github.com/mikelzc1990/nsganetv2) |
| [CATCH: Context-based Meta Reinforcement Learning for Transferrable Architecture Search(Chen et al. 2020) ](https://arxiv.org/abs/2007.09380)
*accepted at ECCV 2020* | Meta-learning
RL | - |
| [Standing on the Shoulders of Giants: Hardware and Neural Architecture Co-Search with Hot Start(Jiang et al. 2020) ](https://arxiv.org/abs/2007.09087)
*accepted at IEEE Transactions On Computer-Aided Design of Integrated Circuits and System* | Model Compression
HotNAS
RL| - |
| [Off-Policy Reinforcement Learning for Efficient and Effective GAN Architecture Search(Tian et al. 2020) ](https://arxiv.org/abs/2007.09180)
*accepted at ECCV 2020* | GAN
RL | [Github](https://github.com/Yuantian013/E2GAN) |
| [Neural Architecture Search for Speech Recognition(Hu et al. 2020) ](https://arxiv.org/abs/2007.08818) | Speech Recognition
DARTS | - |
| [BRP-NAS: Prediction-based NAS using GCNs(Chau et al .2020) ](https://arxiv.org/abs/2007.08668) | Predictor-based
GCN | - |
| [Finding Non-Uniform Quantization Schemes using Multi-Task Gaussian Processes(do Nascimento et al. 2020) ](https://ui.adsabs.harvard.edu/abs/2020arXiv200707743G/abstract)
*accepted at ECCV 2020* | Model Compression
Bayesian Optimization | [Github](https://github.com/ActiveVisionLab/NUQ) |
| [One-Shot Neural Architecture Search via Novelty Driven Sampling(Zhang et al. 2020) ](https://www.ijcai.org/Proceedings/2020/0441.pdf)
*accepted at IJCAI 2020* | Evolutionary
Single-path One-shot | - |
| [Neural Architecture Search in A Proxy Validation Loss Landscape(Li et al. 2020) ](https://proceedings.icml.cc/static/paper_files/icml/2020/439-Paper.pdf)
*accepted at ICML 2020* | Estimation Strategy | - |
| [CP-NAS: Child-Parent Neural Architecture Search for 1-bit CNNs(Zhuo et al. 2020) ](https://www.ijcai.org/Proceedings/2020/0144.pdf)
*accepted at IJCAI 2020* | Model Compression
Binary Networks | - |
| [SI-VDNAS: Semi-Implicit Variational Dropout for Hierarchical One-shot Neural Architecture Search(Wang et al. 2020) ](https://www.ijcai.org/Proceedings/2020/0289.pdf)
*accepted at IJCAI 2020* | Search Strategy | - |
| [An Empirical Study on the Robustness of NAS based Architectures(Devaguptapu et al. 2020) ](https://arxiv.org/abs/2007.08428) | Study | - |
| [MergeNAS: Merge Operations into One for Differentiable Architecture Search(Wang et al. 2020) ](https://www.ijcai.org/Proceedings/2020/0424.pdf)
*accepted at IJCAI 2020* | Search Strategy | - |
| [DropNAS: Grouped Operation Dropout for Differentiable Architecture Search(Hong et al. 2020) ](https://www.ijcai.org/Proceedings/2020/0322.pdf)
*accepted at IJCAI 2020* | Search Strategy | - |
| [Evolving Robust Neural Architectures to Defend from Adversarial Attacks(Kotyan and Vargas 2020) ](http://ceur-ws.org/Vol-2640/paper_1.pdf)
*accepted at Proceedings of the Workshop on Artificial Intelligence Safety 2020* | Adversarial Attacks and Defenses | [Github](https://github.com/shashankkotyan/RobustArchitectureSearch) |
| [Architecture Search of Dynamic Cells for Semantic Video Segmentation(Nekrasov et al. 2020) ](https://openaccess.thecvf.com/content_WACV_2020/html/Nekrasov_Architecture_Search_of_Dynamic_Cells_for_Semantic_Video_Segmentation_WACV_2020_paper.html)
*accepted at WACV 2020* | Video Models
Semantic Segmentation | - |
| [Breaking the Curse of Space Explosion: Towards Efficient NAS with Curriculum Search(Guo et al. 2020) ](https://arxiv.org/abs/2007.07197)
*accepted at ICML 2020* | Search Strategy | - |
| [Towards Automated Neural Interaction Discovery for Click-Through Rate Prediction(Song et al. 2020) ](https://arxiv.org/abs/2007.06434)
*accepted at KDD2020* | CTR | - |
| [MS-NAS: Multi-Scale Neural Architecture Search for Medical Image Segmentation(Yan et al. 2020) ](https://arxiv.org/abs/2007.06151) | Medical
Image Segmentation | - |
| [VINNAS: Variational Inference-based Neural Network Architecture Search(Ferianc et al. 2020) ](https://arxiv.org/abs/2007.06103) | - | - |
| [Multi-Modality Information Fusion for Radiomics-based Neural Architecture Search(Peng et al. 2020) ](https://arxiv.org/abs/2007.06002)
*accepted at MICCAI 2020* | Medical
Multimodal Learning | - |
| [Graph Neural Architecture Search(Gao et al. 2020) ](https://www.researchgate.net/profile/Chuan_Zhou5/publication/342789484_Graph_Neural_Architecture_Search/links/5f0be495299bf18816197d15/Graph-Neural-Architecture-Search.pdf)
*accepted at IJCAI 2020* | GNN
RL | [Github](https://github.com/GraphNAS/GraphNAS) |
| [Ensembles of Networks Produced from Neural Architecture Search(Herron et al. 2020) ](https://keuperj.github.io/MLHPCS/paper/NASEnsemblesFinal.pdf) | Neural Network Ensembles | - |
| [Neural Architecture Search with GBDT(Luo et al. 2020) ](https://arxiv.org/abs/2007.04785) | Predictor-based | [Github](https://github.com/renqianluo/GBDT-NAS) |
| [A Study on Encodings for Neural Architecture Search(White et al. 2020) ](https://arxiv.org/pdf/2007.04965.pdf) | Study
Survey | [Github](https://github.com/naszilla/nas-encodings) |
| [NASGEM: Neural Architecture Search via Graph Embedding Method(Cheng et al. 2020) ](https://arxiv.org/abs/2007.04452) | Estimation Strategy | - |
| [An Evolution-based Approach for Efficient Differentiable Architecture Search(Kobayashi and Nagao) ](https://dl.acm.org/doi/abs/10.1145/3377929.3390003)
*accepted at GECCO 2020* | - | - |
| [HyperFDA: a bi-level Optimization Approach to Neural Architecture Search and Hyperparameters’ optimization via fractal decomposition-based algorithm(Souquet et al. 2020) ](https://dl.acm.org/doi/abs/10.1145/3377929.3390056)
*accepted at GECCO 2020* | - | - |
| [A Multi-objective architecture search for generative adversarial networks(Kobayashi et al. 2020) ](https://dl.acm.org/doi/abs/10.1145/3377929.3390004)
*accepted at GECCO 2020* | GAN | - |
| [A first Step toward Incremental Evolution of Convolutional Neural Networks(Barnes et al. 2020) ](https://dl.acm.org/doi/abs/10.1145/3377929.3389916)
*accepted at GECCO 2020* | - | - |
| [Computational model for neural architecture search(Gottapu 2020) ](https://scholarsmine.mst.edu/cgi/viewcontent.cgi?article=3871&context=doctoral_dissertations) | - | - |
| [Neural Architecture Search for extreme multi-label classification: an evolutionary approach(Pauletto et al. 2020) ](https://hal.archives-ouvertes.fr/hal-02889047/document) | Multi-label Classification | - |
| [Hyperparameter Optimization in Neural Networks via Structured Sparse Recovery(Cho et al. 2020) ](https://arxiv.org/abs/2007.04087) | - | - |
| [Journey Towards Tiny Perceptual Super-Resolution(Lee et al. 2020) ](https://arxiv.org/abs/2007.04356) | Image Translator
Super-Resolution | - |
| [Self-supervised Neural Architecture Search(Kaplan and Giryes 2020) ](https://arxiv.org/abs/2007.01500) | - | - |
| [Multi-Objective Neural Architecture Search Based on Diverse Structures and Adaptive Recommendation(Wang et al. 2020) ](https://arxiv.org/abs/2007.02749) | Multi-objective Search | [Github](https://github.com/wangcn0201/MoARR) |
| [Parametric machines: a fresh approach to architecture search(Vertechi et al. 2020) ](https://arxiv.org/abs/2007.02777) | - | - |
| [Discretization-Aware Architecture Search(Tian et al. 2020) ](https://arxiv.org/abs/2007.03154) | - | - |
| [GOLD-NAS: Gradual, One-Level, Differentiable(Bi et al. 2020) ](https://arxiv.org/abs/2007.03331) | - | - |
| [Surrogate-assisted Particle Swarm Optimisation for Evolving Variable-length Transferable(Wang et al. 2020) ](https://arxiv.org/abs/2007.01556) | Search Strategy | - |
| [M-NAS: Meta Neural Architecture Search(Wang et al. 2020) ](https://aaai.org/ojs/index.php/AAAI/article/view/6084)
*accepted at AAAI 2020* | Meta-learning | - |
| [FiFTy: Large-scale File Fragment Type Identification using Convolutional Neural Networks(Mittal et al. 2020) ](https://ieeexplore.ieee.org/abstract/document/9122499)
*accepted at IEEE Transactions on Information Forensics and Security* | File-type Identification
Forensics | - |
| [RSNet: The Search for Remote Sensing Deep Neural Networks in Recognition Tasks(Wang et al. 2020) ](https://ieeexplore.ieee.org/abstract/document/9123590)
*accepted at IEEE Transactions on Geoscience and Remote Sensing* | Remote Sensing | - |
| [Theory-Inspired Path-Regularized Differential Network Architecture Search(Zhou et al. 2020) ](https://arxiv.org/abs/2006.16537) | Search Strategy | - |
| [The Heterogeneity Hypothesis: Finding Layer-Wise Dissimilated Network Architecture(Li et al. 2020) ](https://arxiv.org/abs/2006.16242) | - | - |
| [Semi-Discrete Optimization Through Semi-Discrete Optimal Transport: A Framework for Neural Architecture Search(Trillos and Morales 2020) ](https://arxiv.org/abs/2006.15221) | - | - |
| [Traditional And Accelerated Gradient Descent for Neural Architecture Search(Trillos et al. 2020) ](https://arxiv.org/abs/2006.15218) | Search Strategy | - |
| [AutoSNAP: Automatically Learning Neural Architectures for Instrument Pose Estimation(Kügler et al. 2020) ](https://arxiv.org/abs/2006.14858)
*accepted at MICCAI 2020* | Pose Estimation | [Github](https://github.com/MECLabTUDA/AutoSNAP) |
| [Evolutionary Recurrent Neural Architecture Search(Tian et al. 2020) ](https://ieeexplore.ieee.org/abstract/document/9129784)
*accepted at IEEE Embedded System Letters* | Search Strategy | - |
| [Neural-Architecture-Search-Based Multiobjective Cognitive Automation System(Wang et al. 2020) ](https://ieeexplore.ieee.org/abstract/document/9127493)
*accepted at IEEE System Journal* | Cognitive Computing
Multi-objective Search | - |
| [Enhancing Model Parallelism in Neural Architecture Search for Multi-device System(Fu et al. 2020) ](https://ieeexplore.ieee.org/abstract/document/9127125)
*accepted at IEEE Micro* | Multi-device System | - |
| [AutoST: Efficient Neural Architecture Search for Spatio-Temporal Prediction(Li et al. 2020) ](http://urban-computing.com/pdf/AutoST_kdd20_camera_ready.pdf)
*accepted at KDD 2020* | Spatio-Temporal Prediction | - |
| [Neural Architecture Search for Sparse DenseNets with Dynamic Compression(O’Neill et al. 2020) ](https://dl.acm.org/doi/abs/10.1145/3377930.3390178)
*accepted at GECCO 2020* | Search Strategy | - |
| [Searching towards Class-Aware Generators for Conditional Generative Adversarial Networks(Zhou et al. 2020) ](https://arxiv.org/abs/2006.14208) | GAN | [Github](https://github.com/PeterouZh/NAS_cGAN) |
| [Neural Architecture Design for GPU-Efficient Networks(Lin et al. 2020) ](https://arxiv.org/abs/2006.14090) | - | - |
| [Equivalence in Deep Neural Networks via Conjugate Matrix Ensembles(Süzen 2020) ](https://arxiv.org/abs/2006.13687) | - | - |
| [Auto-PyTorch Tabular: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL(Zimmer et al. 2020) ](https://arxiv.org/abs/2006.13799) | - | - |
| [NASTransfer: Analyzing Architecture Transferability in Large Scale Neural Architecture Search(Panda et al. 2020) ](https://arxiv.org/abs/2006.13314) | - | - |
| [Tiny Video Networks: Architecture Search for Efficient Video Models(Piergiovanni et al. 2020) ](https://research.google/pubs/pub49263/)
*accepted at 7th ICML Workshop on Automated Machine Learning, 2020* | Video Models | - |
| [FNA++: Fast Network Adaptation via Parameter Remapping and Architecture Search(Fang et al. 2020) ](https://arxiv.org/abs/2006.12986)
*accepted at ICLR 2020* | Semantic Segmentation
Object Detection | [Github](https://github.com/JaminFong/FNA) |
| [Neural networks adapting to datasets: learning network size and topology(Janik and Nowak 2020) ](https://arxiv.org/abs/2006.12195) | - | - |
| [AutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learning(Li et al. 2020) ](https://arxiv.org/abs/2006.11321) | Outlier Detection
Imitation Learning | - |
| [Reinforcement Learning Aided Network Architecture Generation for JPEG Image Steganalysis(Yang et al. 2020) ](https://dl.acm.org/doi/abs/10.1145/3369412.3395060)
*accepted at Proceedings of the 2020 ACM Workshop on Information Hiding and Multimedia Security* | Image Steganalysis | - |
| [Neural Architecture Search for Time Series Classification(Rakhshani et al. 2020) ](https://germain-forestier.info/publis/ijcnn2020.pdf)
*accepted at ijcnn 2020* | Time Series | - |
| [Cyclic Differentiable Architecture Search(Yu et al. 2020) ](https://arxiv.org/abs/2006.10724) | Search Strategy | [Github](https://github.com/researchmm/CDARTS) |
| [Differentially-private Federated Neural Architecture Search(Singh et al. 2020) ](https://arxiv.org/abs/2006.10559) | Federated Learning | [Github](https://github.com/UCSD-AI4H/DP-FNAS) |
| [DrNAS: Dirichlet Neural Architecture Search(Chen et al. 2020) ](https://arxiv.org/abs/2006.10355) | Search Strategy | [Github](https://github.com/xiangning-chen/DrNAS) |
| [Neural Architecture Optimization with Graph VAE(Li et al. 2020) ](https://arxiv.org/abs/2006.10310) | Estimation Strategy
VAE
GNN | - |
| [Fine-Grained Stochastic Architecture Search(Chaudhuri et al. 2020) ](https://arxiv.org/abs/2006.09581) | Search Strategy | - |
| [Bonsai-Net: One-Shot Neural Architecture Search via Differentiable Pruners(Geada et al. 2020) ](https://arxiv.org/abs/2006.09264) | Search Strategy | [Github](https://github.com/RobGeada/bonsai-net-lite) |
| [AlphaGAN: Fully Differentiable Architecture Search for Generative Adversarial Networks(Tian et al. 2020) ](https://arxiv.org/abs/2006.09134) | GAN
DARTS | [Github](https://github.com/yuesongtian/AlphaGAN) |
| [Fine-Tuning DARTS for Image Classification(Tanveer et al. 2020) ](https://arxiv.org/abs/2006.09042) | DARTS | - |
| [Neural Anisotropy Directions(Ortiz-Jiménez et al. 2020) ](https://arxiv.org/abs/2006.09717) | - | - |
| [CryptoNAS: Private Inference on a ReLU Budget(Ghodsi et al. 2020) ](https://arxiv.org/abs/2006.08733) | Private Inference | - |
| [Heuristic Architecture Search Using Network Morphism for Chest X-Ray Classification(Radiuk and Kutucu 2020) ](http://ceur-ws.org/Vol-2623/paper11.pdf) | Medical
Network Morphism | - |
| [Task-aware Performance Prediction for Efficient Architecture Search(Kokiopoulou et al. 2020) ](http://ecai2020.eu/papers/256_paper.pdf)
*accepted at ECAI 2020* | Estimation Strategy | - |
| [Beyond Network Pruning: a Joint Search-and-Training Approach(Lu et al. 2020) ](http://see.xidian.edu.cn/faculty/wsdong/Papers/Conference/ijcai20.pdf)
*accepted at IJCAI 2020* | Multi-objective Search | - |
| [Neural Ensemble Search for Performant and Calibrated Predictions(Zaidi et al. 2020) ](https://arxiv.org/abs/2006.08573) | Ensemble | - |
| [Multi-fidelity Neural Architecture Search with Knowledge Distillation(Trofimov et al. 2020) ](https://arxiv.org/abs/2006.08341) | Estimation Strategy | - |
| [Differentiable Neural Architecture Transformation for Reproducible Architecture Improvement(Kim et al. 2020) ](https://arxiv.org/pdf/2006.08231.pdf) | - | - |
| [Optimal Transport Kernels for Sequential and Parallel Neural Architecture Search(Nguyen et al. 2020) ](https://arxiv.org/abs/2006.07593) | Search Strategy | - |
| [Neural Architecture Search using Bayesian Optimisation with Weisfeiler-Lehman Kernel(Ru et al. 2020) ](https://arxiv.org/abs/2006.07556) | Search Strategy | - |
| [NAS-Bench-NLP: Neural Architecture Search Benchmark for Natural Language Processing(Klyuchnikov et al. 2020) ](https://arxiv.org/abs/2006.07116) | NLP
Benchmark | - |
| [Few-shot Neural Architecture Search(Zhao et al. 2020) ](https://arxiv.org/abs/2006.06863) | Estimation Strategy | - |
| [NADS: Neural Architecture Distribution Search for Uncertainty Awareness(Ardywibowo et al. 2020) ](https://arxiv.org/abs/2006.06646) | - | - |
| [Towards Efficient Automated Machine Learning(Li 2020) ](http://reports-archive.adm.cs.cmu.edu/anon/ml2020/CMU-ML-20-104.pdf) | Survey | - |
| [AMER: Automatic Behavior Modeling and Interaction Exploration in Recommender System(Zhao et al. 2020) ](https://arxiv.org/abs/2006.05933) | CTR | - |
| [Neuroevolution in Deep Neural Networks: Current Trends and Future Challenges(Galvan and Mooney 2020) ](https://arxiv.org/abs/2006.05415) | Survey | - |
| [AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks(Fu et al. 2020) ](https://arxiv.org/abs/2006.08198)
*accepted at ICML 2020* | GAN
Image Translator | [Github](https://github.com/VITA-Group/AGD) |
| [Does Unsupervised Architecture Representation Learning Help Neural Architecture Search?(Yan et al. 2020) ](https://arxiv.org/abs/2006.06936) | Unsupervised
Search Strategy | - |
| [Hardware-Aware Transformable Architecture Search with Efficient Search Space(Jiang et al. 2020) ](https://ieeexplore.ieee.org/abstract/document/9102721)
*accepted at accpeted at ICME 2020* | Search Space
Multi-objective Search | - |
| [Sparse CNN Archtitecture Search(Yeshwanth et al. 2020) ](https://ieeexplore.ieee.org/abstract/document/9102879)
*accepted at ICME 2020* | - | - |
| [Auto-Generating Neural Networks with Reinforcement Learning for Multi-Purpose Image Forensics(Wei et al. 2020) ](https://ieeexplore.ieee.org/abstract/document/9102943)
*accepted at ICME 2020* | Image Forensics | - |
| [Neural Architecture Search without Training(Mellor et al. 2020) ](https://arxiv.org/abs/2006.04647) | Estimation Strategy | [Github](https://github.com/BayesWatch/nas-without-training) |
| [Revisiting the Train Loss: an Efficient Performance Estimator for Neural Architecture Search(Ru et al. 2020) ](https://arxiv.org/abs/2006.04492) | Estimation Strategy | - |
| [Differentiable Neural Input Search for Recommender Systems(Cheng et al. 2020) ](https://arxiv.org/abs/2006.04466) | CTR | - |
| [Efficient Architecture Search for Continual Learning(Gao et al. 2020) ](https://arxiv.org/abs/2006.04027) | Continual Learning | - |
| [Conditional Neural Architecture Search(Kao et al. 2020) ](https://arxiv.org/abs/2006.03969) | Search Strategy
GAN | - |
| [AutoHAS: Differentiable Hyper-parameter and Architecture Search(Dong et al. 2020) ](https://arxiv.org/abs/2006.03656) | - | - |
| [Modeling Task-based fMRI Data via Deep Belief Network with Neural Architecture Search(Qiang et al. 2020) ](https://www.sciencedirect.com/science/article/abs/pii/S0895611120300501)
*accepted at Computerized Medical Imaging and Graphics* | Medical
Deep Belief Network | - |
| [Fast Hardware-Aware Neural Architecture Search(Zhang et al. 2020) ](http://openaccess.thecvf.com/content_CVPRW_2020/html/w40/Zhang_Fast_Hardware-Aware_Neural_Architecture_Search_CVPRW_2020_paper.html)
*accepted at CVPR 2020 workshop* | Multi-objective Search | - |
| [Memory-Efficient Hierarchical Neural Architecture Search for Image Denoising(Zhang et al. 2020) ](http://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Memory-Efficient_Hierarchical_Neural_Architecture_Search_for_Image_Denoising_CVPR_2020_paper.html)
*accepted at CVPR 2020* | Image Translator | |
| [GP-NAS: Gaussian Process based Neural Architecture Search(Li et al. 2020) ](http://openaccess.thecvf.com/content_CVPR_2020/html/Li_GP-NAS_Gaussian_Process_Based_Neural_Architecture_Search_CVPR_2020_paper.html)
*accepted at CVPR 2020* | Search Strategy | - |
| [MemNAS: Memory-Efficient Neural Architecture Search with Grow-Trim Learning(Liu et al.2020) ](http://openaccess.thecvf.com/content_CVPR_2020/html/Liu_MemNAS_Memory-Efficient_Neural_Architecture_Search_With_Grow-Trim_Learning_CVPR_2020_paper.html)
*accepted at CVPR 2020* | Multi-objective Search | [Github](https://github.com/MemNAS/MemNAS) |
| [Can weight sharing outperform random architecture search? An investigation with TuNAS(Bender et al. 2020) ](http://openaccess.thecvf.com/content_CVPR_2020/html/Bender_Can_Weight_Sharing_Outperform_Random_Architecture_Search_An_Investigation_With_CVPR_2020_paper.html)
*accepted at CVPR 2020* | Estimation Strategy | - |
| [Butterfly Transform: An Efficient FFT Based Neural Architecture Design(Alizadeh vahid et al. 2020) ](http://openaccess.thecvf.com/content_CVPR_2020/html/vahid_Butterfly_Transform_An_Efficient_FFT_Based_Neural_Architecture_Design_CVPR_2020_paper.html)
*accepted at CVPR 2020* | Model Compression | [Github](https://github.com/keivanalizadeh/ButterflyTransform) |
| [APQ: Joint Search for Network Architecture, Pruning and Quantization Policy(Wang et al.2020) ](https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_APQ_Joint_Search_for_Network_Architecture_Pruning_and_Quantization_Policy_CVPR_2020_paper.html)
*accepted at CVPR 2020* | Multi-objective Search | [Github](https://github.com/mit-han-lab/apq) |
| [SP-NAS: Serial-to-Parallel Backbone Search for Object Detection(Jiang et al. 2020) ](http://openaccess.thecvf.com/content_CVPR_2020/html/Jiang_SP-NAS_Serial-to-Parallel_Backbone_Search_for_Object_Detection_CVPR_2020_paper.html)
*accepted at CVPR 2020* | Object Detection | - |
| [All in One Bad Weather Removal using Architectural Search(Li et al. 2020) ](https://openaccess.thecvf.com/content_CVPR_2020/html/Li_All_in_One_Bad_Weather_Removal_Using_Architectural_Search_CVPR_2020_paper.html)
*accepted at CVPR 2020* | Image Translator | - |
| [NeuralScale: Efficient Scaling of Neurons for Resource-Constrained Deep Neural Networks(Lee and Lee) ](http://openaccess.thecvf.com/content_CVPR_2020/papers/Lee_NeuralScale_Efficient_Scaling_of_Neurons_for_Resource-Constrained_Deep_Neural_Networks_CVPR_2020_paper.pdf)
*accepted at CVPR 2020* | Model Compression | [Github](https://github.com/eugenelet/NeuralScale) |
| [On Network Design Spaces for Visual Recognition(Radosavovic et al. 2020) ](https://openaccess.thecvf.com/content_ICCV_2019/html/Radosavovic_On_Network_Design_Spaces_for_Visual_Recognition_ICCV_2019_paper.html)
*accepted at ICCV 2019* | Image Recognition | [Github](https://github.com/facebookresearch/pycls) |
| [A Comprehensive Survey of Neural Architecture Search: Challanges and Solutions(Ren et al. 2020) ](https://arxiv.org/abs/2006.02903) | Survey | - |
| [FBNetV3: Joint Architecture-Recipe Search using Neural Acquisition Function(Dai et al. 2020) ](https://arxiv.org/abs/2006.02049) | - | - |
| [Neural Architecture Search With Reinforce And Masked Attention Autoregressive Density Estimators(Krishna et al. 2020) ](https://arxiv.org/abs/2006.00939) | Search Strategy | - |
| [Automation of Deep Learning – Theory and Practice(Wistuba et al. 2020) ](https://dl.acm.org/doi/abs/10.1145/3372278.3390739)
*accepted at ICMR 2020* | Survey | - |
| [AdaEn-Net: An Ensemble of Adaptive 2D-3D Fully Convolutional Networks for Medical Image Segmentation(Baldeon Calisto and Lai-Yuen. 2020) ](https://www.sciencedirect.com/science/article/pii/S0893608020300848)
*accepted at Neural Networks* | Medical
Image Segmentation | - |
| [DC-NAS: Divide-and-Conquer Neural Architecture Search(Wang et al. 2020) ](https://arxiv.org/abs/2005.14456) | Search Strategy | - |
| [HourNAS: Extremely Fast Neural Architecture Search Through an Hourglass Lens(Yang et al. 2020) ](https://arxiv.org/abs/2005.14446) | - | - |
| [Designing Resource-Constrained Neural Networks Using Neural Architecture Search Targeting Embedded Devices(Cassimon et al. 2020) ](https://www.sciencedirect.com/science/article/pii/S2542660520300676)
*accepted at IEEE Internet of Things* | Multi-objective Search | - |
| [Searching Better Architectures for Neural Machine Translation(Fan et al. 2020) ](https://ieeexplore.ieee.org/abstract/document/9095246)
*accepted at IEEE/ACM Transactions on Audio, Speech, and Language Processing* | Language Modeling
Machine Translation | - |
| [Automated Design of Neural Network Architectures with Reinforcement Learning for Detection of Global Manipulations(Chen et al. 2020) ](https://ieeexplore.ieee.org/abstract/document/9103245)
*accepted at IEEE Journal of Selected Topics in Signal Processing* | Object Detection | - |
| [A New Deep Neural Architecture Search Pipeline for Face Recognition(Zhu et al. 2020) ](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9091879)
*accepted at IEEE Access* | Face Recognition
| - |
| [Regularized Evolution for Marco Neural Architecture Search(Kyriakides and Margaritis) ](https://link.springer.com/chapter/10.1007/978-3-030-49186-4_10)
*accepted at AIAI2020* | Search Strategy | - |
| [Evolutionary NAS with Gene Expression Programming of Cellular Encoding(Broni-Bediako et al. 2020) ](https://arxiv.org/abs/2005.13110) | - | - |
| [Synthetic Petri Dish: A Novel Surrogate Model for Rapid Architecture Search(Rawal et al. 2020) ](https://arxiv.org/abs/2005.13092) | Search Strategy
Estimation Strategy | [Github](https://github.com/uber-research/Synthetic-Petri-Dish) |
| [Designing Convolutional Neural Network Architectures Using Cartesian Genetic Programming(Suganuma et al. 2020) ](https://link.springer.com/chapter/10.1007/978-981-15-3685-4_7)
*accepted at accepted in book on “Deep Neural Evolution”* | Search Strategy | - |
| [An Introduction to Neural Architecture Search for Convolutional Networks(Kyriakides and Margaritis, 2020) ](https://arxiv.org/abs/2005.11074) | Survey | - |
| [AutoSegNet: An Automated Neural Network for Image Segmentation(Xu et al. 2020) ](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9095283)
*accepted at IEEE Access* | Medical
Image Segmentation | - |
| [DMS: Differentiable Dimension Search for Binary Neural Networks(Li et al. 2020) ](https://xhplus.github.io/publication/conference-paper/iclr2020/dms/DMS.pdf)
*accepted at 1st Workshop on Neural Architecture Search at ICLR 2020* | Search Strategy
Binary Networks | - |
| [Evolving Deep Neural Networks for X-ray Based Detection of Dangerous Objects(Tsukada et al. 2020) ](https://link.springer.com/chapter/10.1007/978-981-15-3685-4_12)
*accepted at accepted in book on “Deep Neural Evolution”* | Medical | - |
| [Powering One-shot Topological NAS with Stabilized Share-parameter Proxy(Guo et al. 2020) ](https://arxiv.org/abs/2005.10511) | - | - |
| [Optimize CNN Model for FMRI Signal Classification Via Adanet-Based Neural Architecture Search(Dai et al. 2020) ](https://ieeexplore.ieee.org/abstract/document/9098574)
*accepted at IEEE ISBI* | Medical | - |
| [Rethinking Performance Estimation in Neural Architecture Search(Zheng et al. 2020) ](https://arxiv.org/abs/2005.09917)
*accepted at CVPR 2020* | Estimation Strategy | [Github](https://github.com/CVPR2020-ID1073/Rethinking-Performance-Estimation-in-Neural-Architecture-Search) |
| [Application of a genetic algorithm to search for the optimal convolutional neural network architecture with weight distribution(Radiuk 2020) ](http://elar.khnu.km.ua/jspui/bitstream/123456789/8960/1/%D0%A0%D0%90%D0%94%D0%AE%D0%9A.pdf) | - | - |
| [HNAS: Hierarchical Neural Architecture Search on Mobile Devices(Xia et al. 2020) ](https://arxiv.org/abs/2005.07564) | Search Strategy
Model Compression | - |
| [Improving Neuroevolution Using Island Extinction And Repopulation(Lyu et al. 2020) ](https://arxiv.org/abs/2005.07376) | Time Series
Evolutionary | - |
| [You Only Search Once: A Fast Automation Framework for Single-Stage DNN/Accelerator Co-design(Chen et al. 2020) ](https://arxiv.org/abs/2005.07075) | Multi-objective Search | - |
| [DARTS-ASR: Differentiable Architecture Search for Multilingual Speech Recognition and Adaptation(Chen et al. 2020) ](https://arxiv.org/abs/2005.07029)
*accepted at INTERSPEECH 2020* | Speech Recognition
DARTS | - |
| [A Semi-Supervised Assessor of Neural Architectures(Tang et al. 2020) ](https://arxiv.org/abs/2005.06821)
*accepted at CVPR 2020* | Estimation Strategy
GNN | - |
| [Neural Architecture Search for Gliomas Segmentation on Multimodal Magnetic Resonance Imaging(Wang et al. 2020) ](https://arxiv.org/abs/2005.06338) | Medical | - |
| [Binarizing MobileNet via Evolution-based Searching(Phan et al. 2020) ](https://arxiv.org/abs/2005.06305) | Model Compression | - |
| [Neural Architecture Transfer(Lu et al. 2020) ](https://arxiv.org/abs/2005.05859) | Transfer Learning
Evolutionary | [Github](https://github.com/human-analysis/neural-architecture-transfer) |
| [Optimization of deep neural networks: a survey and unified taxonomy(Talbi 2020) ](https://hal.inria.fr/hal-02570804/document) | Survey | - |
| [Auto-Fas: Searching Lightweight Networks for Face Anti-Spoofing(Yu et al. 2020) ](https://ieeexplore.ieee.org/abstract/document/9053587)
*accepted at accetped at ICASSP 2020* | Face Anti-spoofing
Model Compression | - |
| [Neuro Evolutional with Game-Driven Cultural Algorithms(Waris and Reynolds 2020) ](https://www.researchgate.net/profile/Faisal_Waris/publication/341099885_Neuro_Evolutional_with_Game-Driven_Cultural_Algorithms/links/5eadf89c45851592d6b4a953/Neuro-Evolutional-with-Game-Driven-Cultural-Algorithms.pdf)
*accepted at ACM GECCO 2020* | Game Playing
Search Strategy | - |
| [NASIL: Neural Architecture Search With Imitation Learning(Fard et al. 2020) ](https://ieeexplore.ieee.org/document/9054748)
*accepted at ICASSP 2020* | Imitation Learning
Search Strategy | - |
| [Noisy Differentiable Architecture Search(Chu et al. 2020) ](https://arxiv.org/abs/2005.03566) | Search Strategy | [Github](https://github.com/xiaomi-automl/NoisyDARTS) |
| [AutoSpeech: Neural Architecture Search for Speaker Recognition(Ding et al. 2020) ](https://arxiv.org/abs/2005.03215) | Speech Recognition | [Github](https://github.com/VITA-Group/AutoSpeech) |
| [Learning Architectures from an Extended Search Space for Language Modeling(Li et al. 2020) ](https://arxiv.org/abs/2005.02593)
*accepted at ACL 2020* | Language Modeling
Search Space | - |
| [CP-NAS: Child-Parent Neural Architecture Search for 1-bit CNNs( Zhuo et al. 2020) ](https://arxiv.org/abs/2005.00057) | Model Compression
DARTS | - |
| [Particle Swarm Optimization for Evolving Deep Convolutional Neural Networks for Image Classification: Single- and Multi-Objective Approaches(Wang et al. 2020) ](https://link.springer.com/chapter/10.1007/978-981-15-3685-4_6)
*accepted at accepted in book on “Deep Neural Evolution”* | Search Strategy | - |
| [Optimizing Neural Architecture Search using Limited GPU Time in a Dynamic Search Space: A Gene Expression Programming Approach(Alves and de Oliveira. 2020) ](https://arxiv.org/abs/2005.07669)
*accepted at IEEE CEC* | Search Strategy | [Github](https://github.com/jeohalves/nasgep) |
| [Local Search is State of the Art for Neural Architecture Search Benchmarks(White et al. 2020) ](https://arxiv.org/abs/2005.02960)
*accepted at AutoML workshop at ICML’20* | Search Strategy | [Github](https://github.com/realityengines/local_search) |
| [SIPA: A Simple Framework for Efficient Networks(Lee et al. 2020) ](https://arxiv.org/abs/2004.14476) | - | - |
| [The effect of reduced training in neural architecture search(Kyriakides and Margaritis. 2020) ](https://link.springer.com/article/10.1007%2Fs00521-020-04915-6)
*accepted at Neural Comput & Applic* | - | - |
| [Efficient Evolutionary Neural Architecture Search (NAS) by Modular Inheritable Crossover(Tan et al. 2020) ](https://link.springer.com/chapter/10.1007%2F978-981-15-3425-6_61)
*accepted at BIC-TA’20* | Evolutionary | - |
| [MobileDets: Searching for Object Detection Architectures for Mobile Accelerators( Xiong et al. 2020) ](https://arxiv.org/abs/2004.14525) | Object Detection
Model Compression | - |
| [Angle-based Search Space Shrinking for Neural Architecture Search(Hu et al. 2020) ](https://arxiv.org/abs/2004.13431) | - | - |
| [AutoHR: A Strong End-to-end Baseline for Remote Heart Rate Measurement with Neural Searching(Yu et al. 2020) ](https://arxiv.org/abs/2004.12292) | Medical | - |
| [Deep Multimodal Neural Architecture Search(Yu et al. 2020) ](https://arxiv.org/abs/2004.12070) | Multimodal Learning
| - |
| [Depth-Wise Neural Architecture Search(Jordao et al. 2020) ](https://arxiv.org/abs/2004.11178) | - | - |
| [Recurrent Neural Network Architecture Search for Geophyiscal Emulation(Maulik et al. 2020) ](https://arxiv.org/abs/2004.10928) | Emulators
Simulation
Evolutionary | - |
| [Local Search is a Remarkably Strong Baseline for Neural Architecture Search(Ottelander et al. 2020) ](https://arxiv.org/abs/2004.08996) | - | - |
| [Superkernel Neural Architecture Search for Image Denoising(Mozejko et al. 2020) ](https://arxiv.org/abs/2004.08870)
*accepted at NTIRE2020 Workshop at CVPR 2020* | Image Denoising
Unsupervised | - |
| [Organ at Risk Segmentation for Head and Neck Cancer using Stratified Learning and Neural Architecture Search(Guo et al. 2020) ](https://arxiv.org/abs/2004.08426) | Medical
DARTS | - |
| [Fitting the Search Space of Weight-sharing NAS with Graph Convolutional Networks(Chen et al. 2020) ](https://arxiv.org/abs/2004.08423) | - | - |
| [A Neural Architecture Search based Framework for Liquid State Machine Design(Tian et al. 2020) ](https://arxiv.org/abs/2004.07864) | - | - |
| [Geometry-Aware Gradient Algorithms for Neural Architecture Search(Li et al. 2020) ](https://arxiv.org/abs/2004.07802) | - | - |
| [Distributed Evolution of Deep Autoencoders(Hajewski et al. 2020) ](https://arxiv.org/abs/2004.07607) | Distributed System
Evolutionary | - |
| [FBNetV2: Differentiable Neural Architecture Search for Spatial and Channel Dimensions(Wan et al. 2020) ](https://arxiv.org/abs/2004.05565) | - | - |
| [ModuleNet: Knowledge-inherited Neural Architecture Search(Chen et al. 2020) ](https://arxiv.org/abs/2004.05020) | - | - |
| [Evolutionary recurrent neural network for image captioning(Wang et al. 2020) ](https://www.sciencedirect.com/science/article/abs/pii/S0925231220304744)
*accepted at Neurocomputing* | Image Captioning
Cross-modal
Evolutionary | - |
| [Neural Architecture Search for Lightweight Non-Local Networks(Li et al. 2020) ](https://arxiv.org/abs/2004.01961) | - | - |
| [A Generic Graph-based Neural Architecture Encoding Scheme for Predictor-based NAS(Ning et al. 2020) ](https://arxiv.org/abs/2004.01899) | GNN
Predictor-based | - |
| [FedNAS: Federated Deep Learning via Neural Architecture Search(He et al. 2020) ](https://chaoyanghe.com/publications/FedNAS-CVPR2020-NAS.pdf)
*accepted at CVPR 2020 Workshop on Neural Architecture Search and Beyond for Representation Learning* | Federated Learning | [Github](https://github.com/chaoyanghe/FedNAS) |
| [An Evolutionary Approach to Variational Autoencoders(Hajewski and Oliveira. 2020) ](https://ieeexplore.ieee.org/abstract/document/9031239)
*accepted at CCWC’20* | Variational Autoencoder
Unsupervised
Evolutionary | - |
| [A Scalable System for Neural Architecture Search(Hajewski and Oliveira. 2020) ](https://ieeexplore.ieee.org/abstract/document/9031181)
*accepted at CCWC’20* | Distributed System | - |
| [Neural Architecture Generator Optimization(Ru et al. 2020) ](https://arxiv.org/abs/2004.01395) | - | - |
| [Deep-n-Cheap: An Automated Search Framework for Low Complexity Deep Learning(Dey et al. 2020) ](https://arxiv.org/abs/2004.00974) | Bayesian Optimization | [Github](https://github.com/souryadey/deep-n-cheap) |
| [MTL-NAS: Task-Agnostic Neural Architecture Search towards General-Purpose Multi-Task Learning(Gao et al. 2020) ](https://arxiv.org/abs/2003.14058)
*accepted at CVPR’20* | Multi-Task Learning
One-shot
Gradient-based | [Github](https://github.com/bhpfelix/MTLNAS) |
| [Designing Network Design Spaces(Radosavovic et al. 2020) ](https://arxiv.org/abs/2003.13678)
*accepted at CVPR’20* | - | - |
| [Disturbance-immune Weight Sharing for Neural Architecture Search(Niu et al. 2020) ](https://arxiv.org/abs/2003.13089) | - | - |
| [NPENAS:Neural Predictor Guided Evolution for Neural Architecture Search(Wei et al. 2020) ](https://arxiv.org/abs/2003.12857) | - | - |
| [DA-NAS: Data Adapted Pruning for Efficient Neural Architecture Search(Dai et al. 2020) ](https://arxiv.org/abs/2003.12563) | - | - |
| [MiLeNAS: Efficient Neural Architecture Search via Mixed-Level Reformulation(He et al. 2020) ](https://arxiv.org/abs/2003.12238)
*accepted at CVPR’20* | MiLeNAS | [Github](https://github.com/chaoyanghe/MiLeNAS) |
| [Are Labels Necessary for Neural Architecture Search?(Liu et al. 2020) ](https://arxiv.org/abs/2003.12056) | Unsupervised
DARTS | [Github](https://github.com/facebookresearch/unnas) |
| [DCNAS: Densely Connected Neural Architecture Search for Semantic Image Segmentation(Zhang et al. 2020) ](https://arxiv.org/abs/2003.11883) | Image Segmentation | - |
| [Hit-Detector: Hierarchical Trinity Architecture Search for Object Detection(Guo et al. 2020) ](https://arxiv.org/abs/2003.11818)
*accepted at CVPR 2020* | Object Detection
DARTS | [Github](https://github.com/ggjy/HitDet.pytorch) |
| [Sampled Training and Node Inheritance for Fast Evolutionary Neural Architecture Search(Zhang et al. 2020) ](https://arxiv.org/abs/2003.11613) | Evolutionary | - |
| [GreedyNAS: Towards Fast One-Shot NAS with Greedy Supernet(You et al. 2020) ](https://arxiv.org/abs/2003.11236)
*accepted at CVPR’2020* | GreedyNAS | - |
| [BigNAS: Scaling Up Neural Architecture Search with Big Single-Stage Models(Yu et al. 2020) ](https://arxiv.org/abs/2003.11142) | - | - |
| [Steepest Descent Neural Architecture Optimization: Escaping Local Optimum with Signed Neural Splitting(Wu et al. 2020) ](https://arxiv.org/abs/2003.10392) | - | - |
| [BS-NAS: Broadening-and-Shrinking One-Shot NAS with Searchable Numbers of Channels(Shen et al. 2020) ](https://arxiv.org/abs/2003.09821) | - | - |
| [Probabilistic Dual Network Architecture Search on Graphs(Zhao et al. 2020) ](https://arxiv.org/abs/2003.09676) | GNN
Gradient-based | - |
| [GAN Compression: Efficient Architectures for Interactive Conditional GAN(Li et al. 2020) ](https://arxiv.org/abs/2003.08936) | GAN
Model Compression
One-shot | [Github](https://github.com/mit-han-lab/gan-compression) |
| [ElixirNet: Relation-aware Network Architecture Adaptation for Medical Lesion Detection(Jiang et al. 2020) ](https://arxiv.org/abs/2003.08770) | Medical
Lesion Detection | - |
| [Lifelong Learning with Searchable Extension Units(Wang et al. 2020) ](https://arxiv.org/abs/2003.08559) | Lifelong Learning | - |
| [Efficient Backbone Search for Scene Text Recognition(Zhang et al. 2020) ](https://arxiv.org/abs/2003.06567) | Scene Text Recognition | - |
| [AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data(Erickson et al. 2020) ](https://arxiv.org/abs/2003.06505) | Structured Data | - |
| [PONAS: Progressive One-shot Neural Architecture Search for Very Efficient Deployment(Huang and Chu. 2020) ](https://arxiv.org/abs/2003.05112) | - | - |
| [Hierarchical Neural Architecture Search for Single Image Super-Resolution(Guo et al. 2020) ](https://arxiv.org/abs/2003.04619) | Image Translator | - |
| [How to Train Your Super-Net: An Analysis of Training Heuristics in Weight-Sharing NAS(Yu et al. 2020) ](https://arxiv.org/abs/2003.04276) | - | - |
| [AutoML-Zero: Evolving Machine Learning Algorithms From Scratch(Real et al. 2020)](https://arxiv.org/abs/2003.03384)
*accepted at ICML 2020* | AutoML | [Github](https://github.com/google-research/google-research/tree/master/automl_zero#automl-zero) |
| [Accelerator-Aware Neural Network Design Using AutoML(Gupta and Akin. 2020) ](https://arxiv.org/abs/2003.02838)
*accepted at On-device Intelligence Workshop at MLSys’20* | Model Compression | - |
| [Real-time Federated Evolutionary Neural Architecture Search(Zhu and Jin. 2020) ](https://arxiv.org/abs/2003.02793) | Federated Learning
Evolutionary | - |
| [BATS: Binary ArchitecTure Search(Bulat et al. 2020) ](https://arxiv.org/abs/2003.01711)
*accepted at ECCV’20* | Binary Networks
DARTS | [Github](https://github.com/1adrianb/binary-nas) |
| [ADWPNAS: Architecture-Driven Weight Prediction for Neural Architecture Search(Zhang et al. 2020) ](https://arxiv.org/abs/2003.01335) | - | - |
| [NAS-Count: Counting-by-Density with Neural Architecture Search(Hu et al. 2020) ](https://arxiv.org/abs/2003.00217) | - | - |
| [ImmuNetNAS: An Immune-network approach for searching Convolutional Neural Network Architectures(Kefan and Pang. 2020) ](https://arxiv.org/abs/2002.12704) | - | - |
| [Neural Inheritance Relation Guided One-Shot Layer Assignment Search(Meng et al. 2020) ](https://arxiv.org/abs/2002.12580) | - | - |
| [Automatically Searching for U-Net Image Translator Architecture(Shu and Wang. 2020) ](https://arxiv.org/abs/2002.11581) | Image Translator
Evolutionary | - |
| [AutoEmb: Automated Embedding Dimensionality Search in Streaming Recommendations(Zhao et al. 2020) ](https://arxiv.org/abs/2002.11252) | CTR
DARTS | - |
| [Memory-Efficient Models for Scene Text Recognition via Neural Architecture Search(Hong et al. 2020) ](https://openaccess.thecvf.com/content_WACVW_2020/html/w3/Hong_Memory-Efficient_Models_for_Scene_Text_Recognition_via_Neural_Architecture_Search_WACVW_2020_paper.html)
*accepted at WACV’20 workshop* | Scene Text Recognition
Image Recognition
ProxylessNAS | - |
| [Search for Winograd-Aware Quantized Networks(Fernandez-Marques et al. 2020) ](https://arxiv.org/abs/2002.10711) | Model Compression
Winograd
ProxylessNAS | - |
| [Semi-Supervised Neural Architecture Search(Luo et al. 2020) ](https://arxiv.org/abs/2002.10389) | SemiNAS
NAO | [Github](https://github.com/renqianluo/SemiNAS) |
| [Neural Architecture Search for Compressed Sensing Magnetic Resonance Image Reconstruction(Yan et al. 2020) ](https://arxiv.org/abs/2002.09625) | Medical
Magnetic Resonance Imaging
DARTS | [Github](https://github.com/yjump/NAS-for-CSMRI) |
| [DSNAS: Direct Neural Architecture Search without Parameter Retraining(Hu et al. 2020) ](https://arxiv.org/abs/2002.09128) | DSNAS | [Github](https://github.com/SNAS-Series/SNAS-Series) |
| [Neural Architecture Search For Fault Diagnosis(Li et al. 2020) ](https://arxiv.org/abs/2002.07997)
*accepted at ESREL’20* | Fault Diagnosis
RL
Controller-based | - |
| [Learning Architectures for Binary Networks(Kim et al. 2020) ](https://arxiv.org/abs/2002.06963)
*accepted at ECCV’20* | Binary Networks
DARTS | - |
| [Efficient Evolutionary Architecture Search for CNN Optimization on GTSRB(Johner and Wassner. 2020) ](https://ieeexplore.ieee.org/abstract/document/8999305/)
*accepted at ICMLA’19* | Model Compression
Evolutionary | - |
| [Automating Deep Neural Network Model Selection for Edge Inference(Lu et al. 2020) ](https://ieeexplore.ieee.org/abstract/document/8998995)
*accepted at CogMI’20* | Model Compression | - |
| [Neural Architecture Search over Decentralized Data(Xu et al. 2020) ](https://arxiv.org/abs/2002.06352) | Federated Learning
| - |
| [Automatic Structural Search for Multi-task Learning VALPs(Garciarena et al. 2020) ](https://link.springer.com/chapter/10.1007/978-3-030-41913-4_3)
*accepted at OLA’20* | Multi-task Learning | - |
| [RandomNet: Towards Fully Automatic Neural Architecture Design for Multimodal Learning(Alletto et al. 2020) ](https://arxiv.org/abs/2003.01181)
*accepted at Meta-Eval 2020 workshop* | Multimodal Learning | - |
| [Stabilizing Differentiable Architecture Search via Perturbation-based Regularization(Chen and Hsieh. 2020) ](https://arxiv.org/abs/2002.05283) | DARTS | - |
| [Best of Both Worlds: AutoML Codesign of a CNN and its Model Compression(Abdelfattah et al. 2020) ](https://arxiv.org/abs/2002.05022)
*accepted at DAC’20* | Model Compression
RL
| - |
| [Co-Exploration of Neural Architectures and Heterogeneous ASIC Accelerator Designs Targeting Multiple Tasks(Yang et al. 2020) ](https://arxiv.org/abs/2002.04116)
*accepted at DAC’20* | Model Compression
ASIC
RL
Controller-based | - |
| [FPNet: Customized Convolutional Neural Network for FPGA Platforms(Yang et al. 2020) ](https://ieeexplore.ieee.org/abstract/document/8977837)
*accepted at FPT’20* | Model Compression
FPGA
RL
Controller-based | - |
| [AutoFCL: Automatically Tuning Fully Connected Layers for Transfer Learning(Basha et al. 2020) ](https://arxiv.org/abs/2001.11951) | Transfer Learning
CV
Bayesian Optimization | - |
| [NASS: Optimizing Secure Inference via Neural Architecture Search(Bian et al. 2020) ](https://arxiv.org/abs/2001.11854)
*accepted at ECAI’20* | Secure Inference
Privacy
Controller-based| - |
| [Search for Better Students to Learn Distilled Knowledge(Gu et al. 2020) ](https://arxiv.org/abs/2001.11612)
*accepted at ECAI'20* | Model Compression
Knowledge Distillation
DARTS | - |
| [Bayesian Neural Architecture Search using A Training-Free Performance Metric(Camero et al. 2020) ](https://arxiv.org/abs/2001.10726) | RNN
Bayesian Optimization | - |
| [NAS-Bench-1Shot1: Benchmarking and Dissecting One-Shot Neural Architecture Search(Zela et al. 2020) ](https://arxiv.org/abs/2001.10422)
*accepted at ICLR’20* | Benchmark
One-shot | - |
| [Convolution Neural Network Architecture Learning for Remote Sensing Scene Classification(Chen et al. 2010) ](https://arxiv.org/abs/2001.09614) | Remote Sensing
RL | - |
| [Multi-objective Neural Architecture Search via Non-stationary Policy Gradient(Chen et al. 2020) ](https://arxiv.org/abs/2001.08437) | RL
Controller-based | - |
| [Efficient Neural Architecture Search: A Broad Version(Ding et al. 2020) ](https://arxiv.org/abs/2001.06679) | ENAS | - |
| [ENAS U-Net: Evolutionary Neural Architecture Search for Retinal Vessel(Fan et al. 2020) ](https://arxiv.org/abs/2001.06678) | Medical
Image Segmentation
Evolutionary | - |
| [FlexiBO: Cost-Aware Multi-Objective Optimization of Deep Neural Networks(Iqbal et al. 2020) ](https://arxiv.org/abs/2001.06588) | Multi-objective Search
Bayesian Optimization | [Github](https://github.com/softsys4ai/FlexiBO) |
| [Up to two billion times acceleration of scientific simulations with deep neural architecture search(Kasim et al. 2020) ](https://arxiv.org/abs/2001.08055) | Scientific Simulations
ProxylessNAS | - |
| [Latency-Aware Differentiable Neural Architecture Search(Xu et al. 2020) ](https://arxiv.org/abs/2001.06392) | DARTS | - |
| [MixPath: A Unified Approach for One-shot Neural Architecture Search(Chu et al. 2020) ](https://arxiv.org/abs/2001.05887) | One-shot | - |
| [Neural Architecture Search for Skin Lesion Classification(Kwasigroch et al. 2020) ](https://ieeexplore.ieee.org/document/8950333)
*accepted at IEEE Access* | Medical
Image Classification
Network Morphism | - |
| [AdaBERT: Task-Adaptive BERT Compression with Differentiable Neural Architecture Search(Chen et al. 2020) ](https://arxiv.org/abs/2001.04246) | BERT
Model Compression
NLP
DARTS | - |
| [Neural Architecture Search for Deep Image Prior(Ho et al. 2020) ](https://arxiv.org/abs/2001.04776) | Image Denoising
Image Inpainting
Image Super-resolution
Evolutionary | - |
| [Fast Neural Network Adaptation via Parameter Remapping and Architecture Search(Fang et al. 2020) ](https://arxiv.org/abs/2001.02525)
*accepted at ICLR’20* | Object Detection
Image Segmentation
DARTS | [Github](https://github.com/JaminFong/FNA) |
| [FTT-NAS: Discovering Fault-Tolerant Neural Architecture(Li et al. 2020) ](http://nicsefc.ee.tsinghua.edu.cn/media/publications/2020/ASPDAC20_293_6p4Ghq4.pdf)
*accepted at ASP-DAC 2020* | Multi-objective Search
RL | - |
| [Deeper Insights into Weight Sharing in Neural Architecture Search(Zhang et al. 2020) ](https://arxiv.org/abs/2001.01431) | Survey
Weight Sharing
One-shot | - |
| [EcoNAS: Finding Proxies for Economical Neural Architecture Search(Zhou et al. 2020) ](https://arxiv.org/abs/2001.01233)
*accepted at CVPR’20* | Evolutionary | - |
| [DeepMaker: A multi-objective optimization framework for deep neural networks in embedded systems(Loni et al. 2020) ](https://www.sciencedirect.com/science/article/abs/pii/S0141933119301176)
*accepted at Microprocessors and Microsystems* | Multi-objective Search
Evolutionary | - |
| [Auto-ORVNet: Orientation-boosted Volumetric Neural Architecture Search for 3D Shape Classification(Ma et al. 2020) ](https://ieeexplore.ieee.org/abstract/document/8939365)
*accepted at IEEE Access* | 3D Deep learning
DARTS | - |
| [NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search(Dong and Yang et al. 2020) ](https://arxiv.org/abs/2001.00326)
*accepted at ICLR’20* | Benchmark | [Github](https://github.com/D-X-Y/AutoDL-Projects) |
### 2019
- [back to top](#2020)
| Title | Tags | Code |
|:--------|:--------:|:--------:|
| [Scalable NAS with Factorizable Architectural Parameters(Wang et al. 2019) ](https://arxiv.org/abs/1912.13256) | - | - |
| [Modeling Neural Architecture Search Methods for Deep Networks(Malekhosseini et al. 2019) ](https://arxiv.org/abs/1912.13183) | - | - |
| [Searching for Stage-wise Neural Graphs in the Limit(Zhou et al. 2019) ](https://arxiv.org/abs/1912.12860) | - | - |
| [Neural Architecture Search on Acoustic Scene Classification(Li et al. 2019) ](https://arxiv.org/abs/1912.12825) | - | - |
| [RC-DARTS: Resource Constrained Differentiable Architecture Search(Jin et al. 2019) ](https://arxiv.org/abs/1912.12814) | - | - |
| [NAS Evaluation is frustatingly hard(Yang et al. 2019) ](https://arxiv.org/abs/1912.12522)
*accepted at ICLR’20* | - | - |
| [A Genetic Algorithm based Kernel-size Selection Approach for a Multi-column Convolutional Neural Network(Singh et al. 2019) ](https://arxiv.org/abs/1912.12405) | - | - |
| [BetaNAS: Balanced Training and Selective Drop for Neural Architecture Search(Fang et al. 2019) ](https://arxiv.org/abs/1912.11191) | - | - |
| [Progressive DARTS: Bridging the Optimization Gap for NAS in the Wild(Chen et al. 2019) ](https://arxiv.org/abs/1912.10952) | - | - |
| [TextNAS: A Neural Architecture Search Space tailored for Text Representation(Wang et al. 2019) ](https://arxiv.org/abs/1912.10729) | - | - |
| [AtomNAS: Fine-Grined End-To-End Neural Architecture Search(Mei et al. 2019) ](https://arxiv.org/abs/1912.09640)
*accepted at ICLR’20* | - | - |
| [C2FNAS: Coarse-to-Fine Neural Architecture Search for 3D Medical Image Segmentation(Yu et al. 2019) ](https://arxiv.org/abs/1912.09628) | Medical
Image Segmentation | - |
| [A Reinforcement Neural Architecture Search Method for Rolling Bearing Fault Diagnosis(Wang et al. 2019) ](https://www.sciencedirect.com/science/article/pii/S0263224119312849)
*accepted at Measurement* | - | - |
| [Neural Architecture Search for Optimizing Deep Belief Network Models of fMRI Data(Quiang et al. 2019) ](https://link.springer.com/chapter/10.1007/978-3-030-37969-8_4)
*accepted at MMMI’19* | - | - |
| [QoS-aware Neural Architecture Search(Cheng et al. 2019) ](http://mlforsystems.org/assets/papers/neurips2019/qosnas_cheng_2019.pdf)
*accepted at NeurIPS’19* | - | - |
| [Neural-Hardware Architecture Search(Lin et al. 2019) ](http://mlforsystems.org/assets/papers/neurips2019/neural_hardware_lin_2019.pdf)
*accepted at NeurIPS’19* | - | - |
| [Preventing Information Leakage with Neural Architecture Search(Zhang et al. 2019) ](https://arxiv.org/abs/1912.08421) | - | - |
| [Generative Teaching Networks: Accelerating Neural Architecture Search by Learning to Generate Synthetic Training Data(Such et al. 2019) ](https://arxiv.org/abs/1912.07768) | - | - |
| [UNAS: Differentiable Architecture Search Meets Reinforcement Learning(Vahdat et al. 2019) ](https://arxiv.org/abs/1912.07651) | - | - |
| [Efficient network architecture search via multiobjective particle swarm optimization based on decomposition(Jiang et al. 2019) ](https://www.sciencedirect.com/science/article/abs/pii/S0893608019303971) | - | - |
| [Deep Uncertainty Estimation for Model-based Neural Architecture Search(White et al. 2019) ](http://bayesiandeeplearning.org/2019/papers/26.pdf)
*accepted at workshop on Bayesian Deep Learning at NeurIPS’19* | - | - |
| [A Variational-Sequential Graph Autoencoder for Neural Architecture Performance Prediction(Friede et al. 2019) ](https://arxiv.org/abs/1912.05317) | - | - |
| [STEERAGE: Synthesis of Neural Networks Using Architecture Search and Grow-and-Prune Methods(Hassantabar et al. 2019) ](https://arxiv.org/abs/1912.05831) | - | - |
| [Leveraging End-to-End Speech Recognition with Neural Architecture Search(Baruwa et al. 2019) ](https://arxiv.org/abs/1912.05946) | - | - |
| [Efficient Differentiable Neural Architecture Search with Meta Kernels(Chen et al. 2019) ](https://arxiv.org/abs/1912.04749) | - | - |
| [Neural architecture search for image saliency fusion(Bianco et al. 2019) ](https://www.sciencedirect.com/science/article/abs/pii/S1566253519302374)
*accepted at Information Fusion* | - | - |
| [Ultrafast Photorealistic Style Transfer via Neural Architecture Search(An et al. 2019) ](https://arxiv.org/abs/1912.02398) | - | - |
| [AdversarialNAS: Adversarial Neural Architecture Search for GANs(Gao et al. 2019) ](https://arxiv.org/abs/1912.02037) | - | - |
| [MetAdapt: Meta-Learned Task-Adaptive Architecture for Few-Shot Classification(Doveh et al. 2019) ](https://arxiv.org/abs/1912.00412) | - | - |
| [SGAS: Sequential Greedy Architecture Search(Li et al. 2019) ](https://arxiv.org/abs/1912.00195)
*accepted at CVPR’20* | - | - |
| [Blockwisely Supervised Neural Architecture Search with Knowledge Distillation(Li et al. 2019) ](https://arxiv.org/abs/1911.13053) | - | - |
| [Towards Oracle Knowledge Distillation with Neural Architecture Search(Kang et al. 2019) ](https://arxiv.org/abs/1911.13019) | - | - |
| [AutoML for Architecting Efficient and Specialized Neural Networks(Cai et al. 2019) ](https://ieeexplore.ieee.org/abstract/document/8897011)
*accepted at IEEE Micro* | - | - |
| [Artificial Neural Network and Accelerator Co-design using Evolutionary Algorithms(Colangelo et al. 2019) ](https://ieeexplore.ieee.org/abstract/document/8916533)
*accepted at HPEC’19* | - | - |
| [Auto-creation of Effective Neural Network Architecture by Evolutionary Algorithm and ResNet for Image Classification(Chen et al. 2019) ](https://ieeexplore.ieee.org/abstract/document/8914267)
*accepted at SMC’19* | - | - |
| [Performance Prediction Based on Neural Architecture Features(Long et al. 2019) ](https://ieeexplore.ieee.org/abstract/document/8901943)
*accepted at CCHI’19* | - | - |
| [Fair DARTS: Eliminating Unfair Advantages in Differentiable Architecture Search(Chu et al. 2019) ](https://arxiv.org/abs/1911.12126)
*accepted at ECCV’20* | - | - |
| [EDAS: Efficient and Differentiable Architecture Search(Hong et al. 2019) ](https://arxiv.org/abs/1912.01237) | - | - |
| [SGAS: Sequential Greedy Architecture Search(Li et al. 2019) ](https://arxiv.org/abs/1912.00195) | - | - |
| [Ranking architectures using meta-learning(Dubatovka et al. 2019) ](https://arxiv.org/abs/1911.11481) | - | - |
| [Meta-Learning of Neural Architectures for Few-Shot Learning(Elsken et al. 2019) ](https://arxiv.org/abs/1911.11090) | - | - |
| [When NAS Meets Robustness: In Search of Robust Architectures against Adversarial Attacks(Guo et al. 2019) ](https://arxiv.org/abs/1911.10695) | - | - |
| [Exploiting Operation Importance for Differentiable Neural Architecture Search(Xie et al. 2019) ](https://arxiv.org/abs/1911.10511) | - | - |
| [SM-NAS: Structural-to-Modular Neural Architecture Search for Object Detection(Yao et al. 2019) ](https://arxiv.org/abs/1911.09929) | - | - |
| [Multi-Objective Neural Architecture Search via Predictive Network Performance Optimization(Shi et al. 2019) ](https://arxiv.org/abs/1911.09336) | - | - |
| [Data Proxy Generation for Fast and Efficient Neural Architecture Search(Park. 2019) ](https://arxiv.org/abs/1911.09322) | - | - |
| [AutoShrink: A Topology-aware NAS for Discovering Efficient Neural Architecture(Zhang et al. 2019) ](https://arxiv.org/abs/1911.09251) | - | - |
| [Search to Distill: Pearls are Everywhere but not the Eyes(Liu et al. 2019) ](https://arxiv.org/abs/1911.09074) | - | - |
| [EfficientDet: Scalable and Efficient Object Detection(EfficientDet: Scalable and Efficient Object Detection) ](https://arxiv.org/abs/1911.09070) | - | - |
| [Periodic Spectral Ergodicity: A Complexity Measure for Deep Neural Networks and Neural Architecture Search(Süzen et al. 2019) ](https://arxiv.org/abs/1911.07831) | - | - |
| [IMMUNECS: Neural Committee Search by an Artificial Immune System(IMMUNECS: Neural Committee Search by an Artificial Immune System) ](https://arxiv.org/abs/1911.07729) | - | - |
| [NAIS: Neural Architecture and Implementation Search and its Applications in Autonomous Driving(Hao et al. 2019) ](https://arxiv.org/abs/1911.07446) | - | - |
| [Neural Recurrent Structure Search for Knowledge Graph Embedding(Zhang et al. 2019) ](https://arxiv.org/abs/1911.07132) | - | - |
| [S2DNAS: Transforming Static CNN Model for Dynamic Inference via Neural Architecture Search(Yuan et al. 2019) ](https://arxiv.org/abs/1911.07033) | - | - |
| [Automatic Design of CNNs via Differentiable Neural Architecture Search for PolSAR Image Classification(Dong et al. 2019) ](https://arxiv.org/abs/1911.06993) | - | - |
| [Enhancing Neural Architecture Search with Speciation and Inter-Epoch Crossover(Baughmann and Wozniak. 2019) ](https://sc19.supercomputing.org/proceedings/src_poster/src_poster_pages/spostg145.html)
*accepted at Supercomputing’19* | - | - |
| [RAPDARTS: Resource-Aware Progressive Differentiable Architecture Search(Green et al. 2019) ](https://arxiv.org/abs/1911.05704) | - | - |
| [AutoPrune: Automatic Network Pruning by Regularizing Auxiliary Parameters(Xiao et al. 2019) ](http://papers.nips.cc/paper/9521-autoprune-automatic-network-pruning-by-regularizing-auxiliary-parameters.pdf)
*accepted at NeurIPS’19* | - | - |
| [DATA: Differentiable ArchiTecture Approximation(Chang et al. 2019) ](https://papers.nips.cc/paper/8374-data-differentiable-architecture-approximation.pdf)
*accepted at NeurIPS’19* | - | - |
| [Learning to reinforcement learn for Neural Architecture Search(Robles and Vanschoren. 2019) ](https://arxiv.org/pdf/1911.03769.pdf) | - | - |
| [An Automated Approach for Developing a Convolutional Neural Network Using a Modified Firefly Algorithm for Image Classification(Sharaf ad Radwan. 2019) ](https://link.springer.com/chapter/10.1007/978-981-15-0306-1_5)
*accepted at accepted book chapter* | - | - |
| [ENAS Oriented Layer Adaptive Data Scheduling Strategy for Resource Limited Hardware(Li et al. 2019) ](https://www.sciencedirect.com/science/article/abs/pii/S0925231219315620)
*accepted at Neurocomputing Journal* | - | - |
| [Improved Differentiable Architecture Search for Language Modeling and Named Entity Recognition(Jiang et al. 2019) ](https://www.aclweb.org/anthology/D19-1367/)
*accepted at EMNLP-IJCNLP’19* | - | - |
| [Device-Circuit-Architecture Co-Exploration for Computing-in-Memory Neural Accelerators(Jiang et al. 2019) ](https://arxiv.org/abs/1911.00139) | - | - |
| [On Neural Architecture Search for Resource-Constrained Hardware Platforms(Lu et al. 2020) ](https://arxiv.org/abs/1911.00105)
*accepted at ICCAD’19* | - | - |
| [NAT: Neural Architecture Transformer for Accurate and Compact Architectures(Guo et al. 2019) ](https://arxiv.org/abs/1910.14488) | - | - |
| [Deep neural network architecture search using network morphism(Kwasigroch et al. 2019) ](https://ieeexplore.ieee.org/abstract/document/8864624)
*accepted at accepted MMAR’19* | - | - |
| [Person Re-identification with Neural Architecture Search(Zhang et al. 2019) ](https://link.springer.com/chapter/10.1007/978-3-030-31654-9_46)
*accepted at accepted PRCV’19* | - | - |
| [Resource Constrained Neural Network Architecture Search: Will a Submodularity Assumption Help?(Xiong et al. 2019) ](http://openaccess.thecvf.com/content_ICCV_2019/papers/Xiong_Resource_Constrained_Neural_Network_Architecture_Search_Will_a_Submodularity_Assumption_ICCV_2019_paper.html)
*accepted at ICCV’19* | - | - |
| [Auto-FPN: Automatic Network Architecture Adaptation for Object Detection Beyond Classification(Xu et al. 2019) ](https://openaccess.thecvf.com/content_ICCV_2019/html/Xu_Auto-FPN_Automatic_Network_Architecture_Adaptation_for_Object_Detection_Beyond_Classification_ICCV_2019_paper.html)
*accepted at ICCV’19* | Object Detection | - |
| [BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture Search(White et al. 2019) ](https://arxiv.org/abs/1910.11858) | - | - |
| [Stabilizing DARTS with Amended Gradient Estimation on Architectural Parameters(Bi et al. 2019) ](https://arxiv.org/abs/1910.11831) | - | - |
| [An End-to-End HW/SW Co-Design Methodology to Design Efficient Deep Neural Network Systems using Virtual Models(Klaiber et al. 2019) ](https://arxiv.org/abs/1910.11632) | - | - |
| [Hardware-aware one-short Neural Architecture Search in Coordinate Ascent Framework(Hardware-aware one-short Neural Architecture Search in Coordinate Ascent Framework) ](https://arxiv.org/abs/1910.11609) | - | - |
| [Efficient Structured Pruning and Architecture Searching for Group Convolution(Zhao and Luk. 2019) ](http://openaccess.thecvf.com/content_ICCVW_2019/papers/NeurArch/Zhao_Efficient_Structured_Pruning_and_Architecture_Searching_for_Group_Convolution_ICCVW_2019_paper.pdf)
*accepted at ICCV’19 workshop* | - | - |
| [On-Device Image Classification with Proxyless Neural Architecture Search and Quantization-Aware Fine-tuning(Cai et al. 2019) ](http://openaccess.thecvf.com/content_ICCVW_2019/papers/LPCV/Cai_On-Device_Image_Classification_with_Proxyless_Neural_Architecture_Search_and_Quantization-Aware_ICCVW_2019_paper.pdf)
*accepted at ICCV’19 workshop* | - | - |
| [MSNet: Structural Wired Neural Architecture Search for Internet of Things(Cheng et al. 2019) ](http://openaccess.thecvf.com/content_ICCVW_2019/papers/NeurArch/Cheng_MSNet_Structural_Wired_Neural_Architecture_Search_for_Internet_of_Things_ICCVW_2019_paper.pdf)
*accepted at ICCV’19 workshop* | - | - |
| [Efficient Decoupled Neural Architecture Search by Structure and Operation Sampling(Lee et al. 2019) ](https://arxiv.org/abs/1910.10397) | - | - |
| [Using Neural Architecture Search to Optimize Neural Networks for Embedded Devices(Cassimon et al. 2019) ](https://link.springer.com/chapter/10.1007/978-3-030-33509-0_64)
*accepted at 3PGCIC’19* | - | - |
| [NASIB: Neural Architecture Search withIn Budget(Singh et al. 2019) ](https://arxiv.org/abs/1910.08665) | - | - |
| [State of Compact Architecture Search For Deep Neural Networks(Shafiee et al. 2019) ](https://arxiv.org/abs/1910.06466) | - | - |
| [One-Shot Neural Architecture Search via Self-Evaluated Template Network(Dong and Yang. 2019) ](https://arxiv.org/abs/1910.05733) | - | - |
| [Scalable Neural Architecture Search for 3D Medical Image Segmentation(Kim et al. 2019) ](https://arxiv.org/abs/1906.05956)
*accepted at MICCAI’19* | Medical
Image Segmentation | - |
| [Neural Architecture Search for Adversarial Medical Image Segmentation(Dong et al. 2019) ](https://link.springer.com/chapter/10.1007/978-3-030-32226-7_92)
*accepted at MICCAI’19* | Medical
Image Segmentation | - |
| [Searching Learning Strategy with Reinforcement Learning for 3D Medical Image Segmentation(Yang et al. 2019) ](https://link.springer.com/chapter/10.1007/978-3-030-32245-8_1)
*accepted at MICCAI’19* | Medical
Image Segmentation | - |
| [Identify Hierarchical Structures from Task-Based fMRI Data via Hybrid Spatiotemporal Neural Architecture Search Net(Zhang et al. 2019) ](https://link.springer.com/chapter/10.1007/978-3-030-32248-9_83)
*accepted at MICCAI’19* | - | - |
| [Energy-aware Neural Architecture Optimization with Fast Splitting Steepest Descent(Wang et al. 2019) ](https://arxiv.org/abs/1910.03103)
*accepted at accepted EMC2 workshop’19* | - | - |
| [Improving one-shot NAS by Surppressing the Posterior Fading(Li et al. 2019) ](https://arxiv.org/abs/1910.02543) | - | - |
| [Splitting Steepest Descent for Growing Neural Architectures(Liu et al. 2019) ](https://arxiv.org/abs/1910.02366) | - | - |
| [A Novel Automatic CNN Architecture Design Approach Based on Genetic Algorithm(Ahmed et al. 2019) ](https://link.springer.com/chapter/10.1007/978-3-030-31129-2_43)
*accepted at AISI’19* | - | - |
| [RNAS: Architecture Ranking for Powerful Networks(Xu et al. 2019) ](https://arxiv.org/abs/1910.01523) | - | - |
| [Towards Unifying Neural Architecture Space Exploration and Generalization(Bhardwaj and Marculescu) ](https://arxiv.org/abs/1910.00780) | - | - |
| [Sub-Architecture Ensemble Pruning in Neural Architecture Search(Bia et al. 2019) ](https://arxiv.org/abs/1910.00370) | - | - |
| [Towards modular and programmable architecture search(Negrinho et al. 2019) ](https://arxiv.org/abs/1909.13404)
*accepted at NeurIPS’19* | - | - |
| [Automated design of error-resilient and hardware-efficient deep neural networks(Schorn et al. 2019) ](https://arxiv.org/abs/1909.13844) | - | - |
| [STACNAS: Towards Stable and Consistent Optimization for Differentiable Neural Architecture Search(Guilin et al. 2019) ](https://arxiv.org/abs/1909.11926) | - | - |
| [Efficient Residual Dense Block Search for Image Super-Resolution(Song et al. 2019) ](https://arxiv.org/abs/1909.11409) | - | - |
| [Understanding and Improving One-shot Neural Architecture Optimization(Luo et al. 2019) ](https://arxiv.org/abs/1909.10815) | - | - |
| [Scheduled Differentiable Architecture Search for Visual Recognition(Qui et al. 2019) ](https://arxiv.org/abs/1909.10236) | - | - |
| [Understanding and Robustifying Differentiable Architecture Search(Zela et al. 2019) ](https://arxiv.org/abs/1909.09656)
*accepted at ICLR’20* | - | - |
| [Genetic Neural Architecture Search for automatic assessment of human sperm images(Miahi et al. 2019) ](https://arxiv.org/abs/1909.09432) | - | - |
| [IR-NAS: Neural Architecture Search for Image Restoration(Zhang et al. 2019) ](https://arxiv.org/abs/1909.08228) | - | - |
| [Pose Neural Fabrics Search(Yang et al. 2019) ](https://arxiv.org/abs/1909.07068) | - | - |
| [SegNAS3D: Network Architecture Search with Derivative-Free Global Optimization for 3D Image Segmentation(Wong and Moradi. 2019) ](https://arxiv.org/abs/1909.05962) | 3D
Medical
Image Segmentation | - |
| [DARTS+: Improved Differentiable Architecture Search with Early Stopping(Liang et al. 2019) ](https://arxiv.org/abs/1909.06035) | - | - |
| [Searching for Accurate Binary Neural Architectures(Shen et al. 2019) ](https://arxiv.org/abs/1909.07378)
*accepted at ICCV’19 Neural Architects workshop* | - | - |
| [Improving Keyword Spotting and Language Identification via Neural Architecture Search at Scale(Mazzawi et al. 2019) ](https://www.isca-speech.org/archive/Interspeech_2019/pdfs/1916.pdf)
*accepted at INTERSPEECH 2019* | - | - |
| [Neural Architecture Search for Class-incremental Learning(Huang et al. 2019) ](https://arxiv.org/abs/1909.06686) | - | - |
| [Graph-guided Architecture Search for Real-time Semantic Segmentation(Lin et al. 2019) ](https://arxiv.org/abs/1909.06793) | Image Segmentation | - |
| [CARS: Continuous Evolution for Efficient Neural Architecture Search(Yang et al. 2019) ](https://arxiv.org/abs/1909.04977)
*accepted at CVPR’20* | - | - |
| [Bayesian Optimization of Neural Architectures for Human Activity Recognition(Osmani and Hamidi. 2019) ](https://link.springer.com/chapter/10.1007/978-3-030-13001-5_12)
*accepted at Human Activity Sensing* | - | - |
| [Compute-Efficient Neural Network Architecture Optimization by a Genetic Algorithm(Litzinger et al. 2019) ](https://link.springer.com/chapter/10.1007/978-3-030-30484-3_32)
*accepted at ICANN’19* | - | - |
| [Automated deep learning design for medical image classification by health-care professionals with no coding experience: a feasibility study(Faes et al. 2019) ](https://www.sciencedirect.com/science/article/pii/S2589750019301086)
*accepted at The Lancet Digital Health* | - | - |
| [A greedy constructive algorithm for the optimization of neural network architectures(Pasini et al. 2019) ](https://arxiv.org/abs/1909.03306) | - | - |
| [Differentiable Mask Pruning for Neural Networks(Ramakrishnan et al. 2019) ](https://arxiv.org/abs/1909.04567) | - | - |
| [Neural Architecture Search in Embedding Space(Liu. 2019) ](https://arxiv.org/abs/1909.03615) | - | - |
| [Auto-GNN: Neural Architecture Search of Graph Neural Networks(Zhou et al. 2019) ](https://arxiv.org/abs/1909.03184) | - | - |
| [Best Practices for Scientific Research on Neural Architecture Search(Lindauer and Hutter. 2019) ](https://arxiv.org/abs/1909.02453) | - | - |
| [Efficient Neural Architecture Transformation Search in Channel-Level for Object Detection(Peng et al. 2019) ](https://arxiv.org/abs/1909.02293) | - | - |
| [Training compact neural networks via auxiliary overparameterization(Liu et al. 2019) ](https://arxiv.org/abs/1909.02214) | - | - |
| [Rethinking the Number of Channels for Convolutional Neural Networks(Zhu et al. 2019) ](https://arxiv.org/abs/1909.01861) | - | - |
| [MANAS: Multi-Agent Neural Architecture Search(Carlucci et al. 2019) ](https://arxiv.org/abs/1909.01051) | - | - |
| [Resource Optimized Neural Architecture Search for 3D Medical Image Segmentation(Bae et al. 2019) ](https://arxiv.org/abs/1909.00548)
*accepted at MICCAI’19* | Medical
Image Segmentation | - |
| [Neural Architecture Search for Joint Optimization of Predictive Power and Biological Knowledge(Zhang et al. 2019) ](https://arxiv.org/abs/1909.00337) | - | - |
| [Scalable Reinforcement-Learning-Based Neural Architecture Search for Cancer Deep Learning Research(Balaprakash et al. 2019) ](https://arxiv.org/abs/1909.00311)
*accepted at SC’19* | - | - |
| [Automatic Neural Network Search Method for Open Set Recognition(Sun et al. 2019) ](https://ieeexplore.ieee.org/abstract/document/8803605)
*accepted at ICIP’19* | - | - |
| [HM-NAS: Efficient Neural Architecture Search via Hierarchical Masking(Yan et al. 2019) ](https://arxiv.org/abs/1909.00122)
*accepted at ICCV’19 Neural Architects Workshop* | - | - |
| [Once for All: Train One Network and Specialize it for Efficient Deployment(Cai et al. 2019) ](https://arxiv.org/abs/1908.09791) | - | - |
| [Refactoring Neural Networks for Verification(Shriver et al. 2019) ](https://arxiv.org/abs/1908.08026) | - | - |
| [CNASV: A Convolutional Neural Architecture Search-Train Prototype for Computer Vision Task(Zhou and Yang. 2019) ](https://link.springer.com/chapter/10.1007/978-3-030-30146-0_26)
*accepted at CollaborateCom’19* | - | - |
| [Automatic Design of Deep Networks with Neural Blocks(Zhong et al. 2019) ](https://link.springer.com/article/10.1007/s12559-019-09677-5)
*accepted at Cognitive Computation* | - | - |
| [Differentiable Learning-to-Group Channels via Groupable Convolutional Neural Networks(Zhang et al. 2019) ](https://arxiv.org/abs/1908.05867) | - | - |
| [SCARLET-NAS: Bridging the gap Between Scalability and Fairness in Neural Architecture Search(Chu et al. 2019) ](https://arxiv.org/abs/1908.06022) | - | - |
| [A Novel Encoding Scheme for Complex Neural Architecture Search(Ahmad et al. 2019) ](https://ieeexplore.ieee.org/document/8793329)
*accepted at ITC-CSCC* | - | - |
| [A Graph-Based Encoding for Evolutionary Convolutional Neural Network Architecture Design(Irwin-Harris et al. 2019) ](https://ieeexplore.ieee.org/document/8790093)
*accepted at accepted CEC’19* | - | - |
| [A Novel Framework for Neural Architecture Search in the Hill Climbing Domain(Verma et al. 2019) ](https://ieeexplore.ieee.org/abstract/document/8791709)
*accepted at AIKE’19* | - | - |
| [Automated Neural Network Construction with Similarity Sensitive Evolutionary Algorithms(Tian et al. 2019) ](http://rvc.eng.miami.edu/Paper/2019/IRI19_EA.pdf) | - | - |
| [AutoGAN: Neural Architecture Search for Generative Adversarial Networks(Gong et al. 2019) ](https://arxiv.org/abs/1908.03835)
*accepted at ICCV’19* | GAN | [Github](https://github.com/VITA-Group/AutoGAN) |
| [Refining the Structure of Neural Networks Using Matrix Conditioning(Yousefzadeh and O’Leary. 2019) ](https://arxiv.org/abs/1908.02400) | - | - |
| [SqueezeNAS: Fast neural architecture search for faster semantic segmentation(Shaw et al. 2019) ](https://arxiv.org/abs/1908.01748) | Image Segmentation | - |
| [MoGA: Searching Beyond MobileNetV3(Chu et al. 2019) ](https://arxiv.org/abs/1908.01314)
*accepted at ICASSP’20* | - | - |
| [Evolving deep neural networks by multi-objective particle swarm optimization for image classification(Wang et al. 2019) ](https://arxiv.org/abs/1904.09035)
*accepted at GECCO’19* | - | - |
| [Particle Swarm Optimisation for Evolving Deep Neural Networks for Image Classification by Evolving and Stacking Transferable Blocks(Wang et al. 2019) ](https://arxiv.org/abs/1907.12659)
*accepted at IEEE CEC’20* | - | - |
| [Self-Adaptive 2D-3D Ensemble of Fully Convolutional Networks for Medical Image Segmentation(Calisto and Lai-Yuen. 2019) ](https://arxiv.org/abs/1907.11587)
*accepted at SPIE Medical Imaging’20* | Medical
Image Segmentation | - |
| [MemNet: Memory-Efficiency Guided Neural Architecture Search with Augment-Trim learning(by Liu et al. 2019) ](https://arxiv.org/abs/1907.09569) | - | - |
| [Efficient Novelty-Driven Neural Architecture Search(Zhang et al. 2019) ](https://arxiv.org/abs/1907.09109) | - | - |
| [PC-DARTS: Partial Channel Connections for Memory-Efficient Differentiable Architecture Search(Xu et al. 2019) ](https://arxiv.org/abs/1907.05737) | - | - |
| [Hardware/Software Co-Exploration of Neural Architectures(Jiang et al. 2019) ](https://arxiv.org/abs/1907.04650) | - | - |
| [EPNAS: Efficient Progressive Neural Architecture Search(Zhou et al. 2019) ](https://arxiv.org/abs/1907.04648) | - | - |
| [Video Action Recognition via Neural Architecture Searching(Peng et al. 2019) ](https://arxiv.org/abs/1907.04632) | Video Models | - |
| [Hardware/Software Co-Exploration of Neural Architectures(Jiang et al. 2019) ](https://arxiv.org/abs/1907.04650)
*accepted at ASP-DAC’20* | - | - |
| [When Neural Architecture Search Meets Hardware Implementation: from Hardware Awareness to Co-Design(Zhang et al. 2019) ](https://ieeexplore.ieee.org/document/8839421)
*accepted at ISVLSI’19* | - | - |
| [Reinforcement Learning for Neural Architecture Search: A Review(Jaafra et al. 2019 accepted at Image and Vision Computing) ](https://www.sciencedirect.com/science/article/pii/S026288561930088) | - | - |
| [Architecture Search for Image Inpainting(Li and King. 2019. accepted at International Symposium on Neural Networks) ](https://link.springer.com/chapter/10.1007/978-3-030-22796-8_12) | - | - |
| [Neural Network Architecture Search with Differentiable Cartesian Genetic Programming for Regression(Märtens and Izzo. 2019) ](https://arxiv.org/abs/1907.01939) | - | - |
| [FairNAS: Rethinking Evaluation Fairness of Weight Sharing Neural Architecture Search(Chu et al. 2019) ](https://arxiv.org/abs/1907.01845) | - | - |
| [HyperNOMAD: Hyperparameter optimization of deep neural networks using mesh adaptive direct search(Lakhmiri et al. 2019) ](https://arxiv.org/pdf/1907.01698.pdf) | - | - |
| [Surrogate-Assisted Evolutionary Deep Learning Using an End-to-End Random Forest-based Performance Predictor(Sun et al. 2019) ](https://ieeexplore.ieee.org/document/8744404)
*accepted at accepted by IEEE Transactions on Evolutionary Computation* | - | - |
| [Adaptive Genomic Evolution of Neural Network Topologies(Behjat et al. 2019) ](https://arxiv.org/abs/1903.07107)
*accepted at accepted and presented in ICRA 2019* | - | - |
| [Densely Connected Search Space for More Flexible Neural Architecture Search(Fang et al. 2019) ](https://arxiv.org/abs/1906.09607) | - | - |
| [Posterior-Guided Neural Architecture Search(Zhou et al. 2020) ](https://arxiv.org/abs/1906.09557)
*accepted at AAAI* | - | - |
| [SwiftNet: Using Graph Propagation as Meta-knowledge to Search Highly Representative Neural Architectures(Cheng et al. 2019) ](https://arxiv.org/abs/1906.08305) | - | - |
| [Transfer NAS: Knowledge Transfer between Search Spaces with Transformer Agents(Borsos et al. 2019) ](https://arxiv.org/abs/1906.08102) | - | - |
| [XNAS: Neural Architecture Search with Expert Advice(Nayman et al. 2019) ](https://arxiv.org/abs/1906.08031)
*accepted at NeurIPS’19* | - | - |
| [A Study of the Learning Progress in Neural Architecture Search Techniques(Singh et al. 2019) ](https://arxiv.org/abs/1906.07590) | - | - |
| [Hardware aware Neural Network Architectures(Srinivas et al. 2019) ](https://arxiv.org/abs/1906.07214) | - | - |
| [Sample-Efficient Neural Architecture Search by Learning Action Space(Wang et al. 2019) ](https://arxiv.org/abs/1906.06832) | - | - |
| [SwiftNet: Using Graph Propagation as Meta-knowledge to Search Highly Representative Neural Architectures(Cheng et al. 2019) ](https://arxiv.org/abs/1906.08305) | - | - |
| [Automatic Modulation Recognition Using Neural Architecture Search(Wei et al. 2019) ](https://ieeexplore.ieee.org/abstract/document/8735458)
*accepted at accepted High Performance Big Data and Intelligent Systems* | - | - |
| [Continual and Multi-Task Architecture Search(Pasunuru and Bansal. 2019) ](https://arxiv.org/abs/1906.05226) | - | - |
| [AutoGrow: Automatic Layer Growing in Deep Convolutional Networks(Wen et al. 2019) ](https://arxiv.org/abs/1906.02909) | - | - |
| [One-Short Neural Architecture Search via Compressing Sensing(Cho et al. 2019) ](https://arxiv.org/abs/1906.02869) | - | - |
| [V-NAS: Neural Architecture Search for Volumetric Medical Image Segmentation(Zhu et al. 2019) ](https://arxiv.org/abs/1906.02817) | Medical
Image Segmentation | - |
| [StyleNAS: An Empirical Study of Neural Architecture Search to Uncover Surprisingly Fast End-to-End Universal Style Transfer Networks(An et al. 2019) ](https://arxiv.org/abs/1906.02470) | - | - |
| [Efficient Forward Architecture Search(Hu et al. 2019) ](https://arxiv.org/abs/1905.13360)
*accepted at NeurIPS’19* | - | - |
| [Differentiable Neural Architecture Search via Proximal Iterations(Yao et al. 2019) ](https://arxiv.org/abs/1905.13577) | - | - |
| [Dynamic Distribution Pruning for Efficient Network Architecture Search(Zheng et al. 2019) ](https://arxiv.org/abs/1905.13543) | - | - |
| [Particle swarm optimization of deep neural networks architectures for image classification(Fernandes Junior and Yen. 2019. accepted at Swarm and Evolutionary Computation) ](https://www.sciencedirect.com/science/article/abs/pii/S2210650218309246) | - | - |
| [On Network Design Spaces for Visual Recognition(Radosavovic et al. 2019) ](https://arxiv.org/abs/1905.13214)
*accepted at ICCV’20* | - | - |
| [AssembleNet: Searching for Multi-Stream Neural Connectivity in Video Architectures(Ryoo et al. 2019) ](https://arxiv.org/abs/1905.13209) | Video Models | - |
| [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks(Tan and Le) ](http://proceedings.mlr.press/v97/tan19a/tan19a.pdf)
*accepted at ICML’19. 2019* | - | - |
| [Structure Learning for Neural Module Networks(Pahuja et al. 2019) ](https://arxiv.org/abs/1905.11532) | - | - |
| [SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers(Fedorov et al. 2019) ](https://arxiv.org/abs/1905.12107)
*accepted at NeurIPS’19* | - | - |
| [Network Pruning via Transformable Architecture Search(Dong and Yang. 2019) ](https://arxiv.org/abs/1905.09717)
*accepted at NeurIPS’19* | - | - |
| [DEEP-BO for Hyperparameter Optimization of Deep Networks(Cho et al. 2019) ](https://arxiv.org/abs/1905.09680) | - | - |
| [Constrained Design of Deep Iris Networks(Nguyen et al. 2019) ](https://arxiv.org/abs/1905.09481) | - | - |
| [Adaptive Stochastic Natural Gradient Method for One-Shot Neural Architecture Search(Akimoto et al. 2019) ](https://arxiv.org/abs/1905.08537)
*accepted at ICML’19* | - | - |
| [Multinomial Distribution Learning for Effective Neural Architecture Search(Zheng et al. 2019) ](https://arxiv.org/abs/1905.07529) | - | - |
| [EENA: Efficient Evolution of Neural Architecture(Zhu et al. 2019) ](https://arxiv.org/abs/1905.07320)
*accepted at ICCV’19 Neural Architects Workshop* | - | - |
| [DeepSwarm: Optimising Convolutional Neural Networks using Swarm Intelligence(Byla and Pang. 2019) ](https://arxiv.org/abs/1905.07350) | - | - |
| [AutoDispNet: Improving Disparity Estimation with AutoML(Saikia et al. 2019) ](https://arxiv.org/abs/1905.07443) | - | - |
| [Online Hyper-parameter Learning for Auto-Augmentation Strategy(Lin et al. 2019) ](https://arxiv.org/abs/1905.07373) | - | - |
| [Regularized Evolutionary Algorithm for Dynamic Neural Topology Search(Saltori et al. 2019) ](https://arxiv.org/abs/1905.06252) | - | - |
| [Deep Neural Architecture Search with Deep Graph Bayesian Optimization(Ma et al. 2019) ](https://arxiv.org/abs/1905.06159) | - | - |
| [Automatic Model Selection for Neural Networks(Laredo et al. 2019) ](https://arxiv.org/abs/1905.06010) | - | - |
| [Tabular Benchmarks for Joint Architecture and Hyperparameter Optimization(Klein and Hutter. 2019) ](https://arxiv.org/abs/1905.04970) | - | - |
| [BayesNAS: A Bayesian Approach for Neural Architecture Search(Zhou et al. 2019) ](https://arxiv.org/abs/1905.04919)
*accepted at ICML’19* | - | - |
| [Single-Path NAS: Device-Aware Efficient ConvNet Design(Stamoulis et al. 2019) ](https://arxiv.org/abs/1905.04159) | - | - |
| [Automatic Design of Artificial Neural Networks for Gamma-Ray Detection(Assuncao et al. 2019) ](https://arxiv.org/abs/1905.03532) | - | - |
| [Neural Architecture Refinement: A Practical Way for Avoiding Overfitting in NAS(Jiang et al. 2019) ](https://arxiv.org/abs/1905.02341) | - | - |
| [Fast and Reliable Architecture Selection for Convolutional Neural Networks(Hahn et al. 2019) ](https://arxiv.org/abs/1905.01924) | - | - |
| [Differentiable Architecture Search with Ensemble Gumbel-Softmax(Chang et al. 2019) ](https://arxiv.org/abs/1905.01786) | - | - |
| [Searching for A Robust Neural Architecture in Four GPU Hours(Dong and Yang 2019) ](https://xuanyidong.com/publication/cvpr-2019-gradient-based-diff-sampler/)
*accepted at CVPR’19* | - | - |
| [Evolving unsupervised deep neural networks for learning meaningful representations(Sun et al. 2019, accepted by IEEE Transactions on Evolutionary Computation) ](https://arxiv.org/abs/1712.05043) | - | - |
| [Evolving Deep Convolutional Neural Networks for Image Classification(Sun et al. 2019, accepted by IEEE Transactions on Evolutionary Computation) ](https://arxiv.org/abs/1710.10741) | - | - |
| [AdaResU-Net: Multiobjective Adaptive Convolutional Neural Network for Medical Image Segmentation(Baldeon-Calisto and Lai-Yuen. 2019.) ](https://www.sciencedirect.com/science/article/pii/S0925231219304679)
*accepted at Neurocomputing* | Medical
Image Segmentation | - |
| [Automatic Design of Convolutional Neural Network for Hyperspectral Image Classification(Chen et al. 2019) ](https://ieeexplore.ieee.org/abstract/document/8703410)
*accepted at IEEE Transactions on Geoscience and Remote Sensing* | - | - |
| [Progressive Differentiable Architecture Search: Bridging the Depth Gap between Search and Evaluation(Chen et al. 2019) ](https://arxiv.org/abs/1904.12760) | - | - |
| [Design Automation for Efficient Deep Learning Computing(Han et al. 2019) ](https://arxiv.org/abs/1904.10616) | - | - |
| [CascadeML: An Automatic Neural Network Architecture Evolution and Training Algorithm for Multi-label Classification(Pakrashi and Namee 2019) ](https://arxiv.org/abs/1904.10551) | - | - |
| [GraphNAS: Graph Neural Architecture Search with Reinforcement Learning(Gao et al. 2019) ](https://arxiv.org/abs/1904.09981) | - | - |
| [Neural Architecture Search for Deep Face Recognition(Zhu. 2019) ](https://arxiv.org/abs/1904.09523) | - | - |
| [Efficient Neural Architecture Search on Low-Dimensional Data for OCT Image Segmentation(Gessert and Schlaefer. 2019) ](https://openreview.net/forum?id=Syg3FDjntN) | Medical
Image Segmentation | - |
| [NAS-Unet: Neural Architecture Search for Medical Image Segmentation(Weng et al. 2019) ](https://ieeexplore.ieee.org/document/8681706)
*accepted at IEEE Access* | Medical
Image Segmentation | - |
| [Fast DENSER: Efficient Deep NeuroEvolution(Assunção et al. 2019) ](https://link.springer.com/chapter/10.1007/978-3-030-16670-0_13)
*accepted at ECGP’19* | - | - |
| [NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection(Ghaisi et al. 2019) ](https://arxiv.org/abs/1904.07392)
*accepted at CVPR’19* | - | - |
| [Automated Search for Configurations of Deep Neural Network Architectures(Ghamizi et al. 2019) ](https://arxiv.org/abs/1904.04612)
*accepted at SPLC’19* | - | - |
| [WeNet: Weighted Networks for Recurrent Network Architecture Search(Huang and Xiang. 2019) ](https://arxiv.org/abs/1904.03819) | - | - |
| [Resource Constrained Neural Network Architecture Search(Xiong et al. 2019) ](https://arxiv.org/abs/1904.03786) | - | - |
| [Size/Accuracy Trade-Off in Convolutional Neural Networks: An Evolutionary Approach(Cetto et al. 2019) ](https://link.springer.com/chapter/10.1007/978-3-030-16841-4_3)
*accepted at INNSBDDL* | - | - |
| [ASAP: Architecture Search, Anneal and Prune(Noy et al. 2019) ](https://arxiv.org/abs/1904.04123) | - | - |
| [Single-Path NAS: Designing Hardware-Efficient ConvNets in less than 4 Hours(Stamoulis et al. 2019) ](https://arxiv.org/abs/1904.02877) | - | - |
| [Template-Based Automatic Search of Compact Semantic Segmentation Architectures(Nekrasov et al. 2019) ](https://arxiv.org/abs/1904.02365) | Image Segmentation | - |
| [Exploring Randomly Wired Neural Networks for Image Recognition(Xie et al. 2019) ](https://arxiv.org/abs/1904.01569) | - | - |
| [Understanding Neural Architecture Search Techniques(Adam and Lorraine 2019) ](https://arxiv.org/abs/1904.00438) | - | - |
| [Automatic Convolutional Neural Architecture Search for Image Classification Under Different Scenes(Weng et al. 2019) ](https://ieeexplore.ieee.org/document/8676019)
*accepted at accepted for IEEE Access* | - | - |
| [Single Path One-Shot Neural Architecture Search with Uniform Sampling(Guo et al. 2019) ](https://arxiv.org/abs/1904.00420) | - | - |
| [Network Slimming by Slimmable Networks: Towards One-Shot Architecture Search for Channel Numbers(Yu and Huang 2019) ](https://arxiv.org/abs/1903.11728) | - | - |
| [sharpDARTS: Faster and More Accurate Differentiable Architecture Search(Hundt et al. 2019) ](https://arxiv.org/abs/1903.09900) | - | - |
| [DetNAS: Neural Architecture Search on Object Detection(Chen et al. 2019) ](https://arxiv.org/abs/1903.10979)
*accepted at NeurIPS’19* | Object Detection | [megvii-code](https://github.com/megvii-model/DetNAS) |
| [Evolution of Deep Convolutional Neural Networks Using Cartesian Genetic Programming(Suganuma et al. 2019) ](https://www.mitpressjournals.org/doi/abs/10.1162/evco_a_00253)
*accepted at Evolutionary Computation* | - | - |
| [Deep Evolutionary Networks with Expedited Genetic Algorithm for Medical Image Denoising(Liu et al. 2019) ](https://www.sciencedirect.com/science/article/abs/pii/S1361841518307734)
*accepted at Medical Image Analysis* | - | - |
| [Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly(Kandasamy et al. 2019) ](https://arxiv.org/abs/1903.06694) | - | - |
| [AttoNets: Compact and Efficient Deep Neural Networks for the Edge via Human-Machine Collaborative Design(Wong et al. 2019) ](https://arxiv.org/abs/1903.07209) | - | - |
| [Improving Neural Architecture Search Image Classifiers via Ensemble Learning(Macko et al. 2019) ](https://arxiv.org/abs/1903.06236) | - | - |
| [Software-Defined Design Space Exploration for an Efficient AI Accelerator Architecture(Yu et al. 2019) ](https://arxiv.org/abs/1903.07676) | - | - |
| [MFAS: Multimodal Fusion Architecture Search(Pérez-Rúa et al. 2019) ](https://hal.archives-ouvertes.fr/hal-02068293/document)
*accepted at CVPR’19* | Multimodal Learning | - |
| [A Hybrid GA-PSO Method for Evolving Architecture and Short Connections of Deep Convolutional Neural Networks(Wang et al. 2019) ](https://arxiv.org/abs/1903.03893)
*accepted at PRICAI’19* | - | - |
| [Partial Order Pruning: for Best Speed/Accuracy Trade-off in Neural Architecture Search(Li et al. 2019) ](https://arxiv.org/abs/1903.03777) | - | - |
| [Inductive Transfer for Neural Architecture Optimization(Wistuba and Pedapati 2019) ](https://arxiv.org/abs/1903.03536) | - | - |
| [Evolutionary Cell Aided Design for Neural Network(Colangelo et al. 2019) ](https://arxiv.org/abs/1903.02130) | - | - |
| [Automated Architecture-Modeling for Convolutional Neural Networks(Duong 2019) ](https://btw.informatik.uni-rostock.de/download/workshopband/D1-1.pdf) | - | - |
| [Learning Implicitly Recurrent CNNs Through Parameter Sharing(Savarese and Maire) ](https://arxiv.org/abs/1902.09701)
*accepted at ICLR’19* | - | - |
| [Evaluating the Search Phase of Neural Architecture Searc(Sciuto et al. 2019) ](https://arxiv.org/abs/1902.08142) | - | - |
| [Random Search and Reproducibility for Neural Architecture Search(Li and Talwalkar 2019) ](https://arxiv.org/abs/1902.07638) | - | - |
| [Evolutionary Neural AutoML for Deep Learning(Liang et al. 2019) ](https://arxiv.org/abs/1902.06827) | - | - |
| [Fast Task-Aware Architecture Inference(Kokiopoulou et al. 2019) ](https://arxiv.org/abs/1902.05781) | - | - |
| [Probabilistic Neural Architecture Search(Casale et al. 2019) ](https://arxiv.org/abs/1902.05116) | - | - |
| [Investigating Recurrent Neural Network Memory Structures using Neuro-Evolution(Ororbia et al. 2019) ](https://arxiv.org/abs/1902.02390) | - | - |
| [Accuracy vs. Efficiency: Achieving Both through FPGA-Implementation Aware Neural Architecture Search(Jiang et al. 2019) ](https://arxiv.org/abs/1901.11211)
*accepted at DAC’19* | - | - |
| [The Evolved Transformer(So et al. 2019) ](https://arxiv.org/abs/1901.11117) | - | - |
| [Designing neural networks through neuroevolution(Stanley et al. 2019) ](https://www.nature.com/articles/s42256-018-0006-z)
*accepted at Nature Machine Intelligence* | - | - |
| [NeuNetS: An Automated Synthesis Engine for Neural Network Design(Sood et al. 2019) ](https://arxiv.org/abs/1901.06261) | - | - |
| [Fast, Accurate and Lightweight Super-Resolution with Neural Architecture Search(Chu et al. 2019) ](https://arxiv.org/abs/1901.07261)
*accepted at ICPR’20* | - | - |
| [EAT-NAS: Elastic Architecture Transfer for Accelerating Large-scale Neural Architecture Search(Fang et al. 2019) ](https://arxiv.org/abs/1901.05884) | - | - |
| [Bayesian Learning of Neural Network Architectures(Dikov et al. 2019) ](https://arxiv.org/abs/1901.04436) | - | - |
| [Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation(Liu et al. 2019) ](https://arxiv.org/abs/1901.02985)
*accepted at CVPR’19* | Image Segmentation | [Github](https://github.com/MenghaoGuo/AutoDeeplab) |
| [The Art of Getting Deep Neural Networks in Shape(Mammadli et al. 2019) ](https://dl.acm.org/citation.cfm?id=3291053)
*accepted at TACO Journal* | - | - |
| [Multi-Objective Reinforced Evolution in Mobile Neural Architecture Search(Chu et al. 2019) ](https://arxiv.org/abs/1901.01074) | - | - |
### 2018
- [back to top](#2020)
| Title | Tags | Code |
|:--------|:--------:|:--------:|
| [A particle swarm optimization-based flexible convolutional auto-encoder for image classification(Sun et al. 2018, published by IEEE Transactions on Neural Networks and Learning Systems) ](https://arxiv.org/abs/1712.05042) | - | - |
| [SNAS: Stochastic Neural Architecture Search(Xie et al. 2018) ](https://arxiv.org/abs/1812.09926)
*accepted at ICLR’19* | SNAS | [Github](https://github.com/SNAS-Series/SNAS-Series) |
| [Graph Hypernetworks for Neural Architecture Search(Zhang et al. 2018) ](https://arxiv.org/abs/1810.05749)
*accepted at Accepted at ICLR’19* | - | - |
| [Efficient Multi-Objective Neural Architecture Search via Lamarckian Evolution(Elsken et al. 2018) ](https://arxiv.org/abs/1804.09081)
*accepted at ICLR’19* | - | - |
| [Macro Neural Architecture Search Revisited(Hu et al. 2018) ](http://metalearning.ml/2018/papers/metalearn2018_paper16.pdf)
*accepted at Meta-Learn NeurIPS workshop’18* | - | - |
| [AMLA: an AutoML frAmework for Neural Network Design(Kamath et al. 2018) ](http://pkamath.com/publications/papers/amla_automl18.pdf)
*accepted at at ICML AutoML workshop* | - | - |
| [ChamNet: Towards Efficient Network Design through Platform-Aware Model Adaptation(Dai et al. 2018) ](https://arxiv.org/abs/1812.08934) | - | - |
| [Neural Architecture Search Over a Graph Search Space(de Laroussilhe et al. 2018) ](https://arxiv.org/abs/1812.10666) | - | - |
| [A Review of Meta-Reinforcement Learning for Deep Neural Networks Architecture Search(Jaafra et al. 2018) ](https://arxiv.org/abs/1812.07995) | - | - |
| [Evolutionary Neural Architecture Search for Image Restoration(van Wyk and Bosman 2018) ](https://arxiv.org/abs/1812.05866) | - | - |
| [IRLAS: Inverse Reinforcement Learning for Architecture Search(Guo et al. 2018) ](https://arxiv.org/abs/1812.05285)
*accepted at CVPR’19* | - | - |
| [FBNet: Hardware-Aware Efficient ConvNet Designvia Differentiable Neural Architecture Search(Wu et al. 2018) ](https://arxiv.org/abs/1812.03443)
*accepted at CVPR’19* | - | - |
| [ShuffleNASNets: Efficient CNN models throughmodified Efficient Neural Architecture Search(Laube et al. 2018) ](https://arxiv.org/abs/1812.02975) | - | - |
| [ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware(Cai et al. 2018) ](https://arxiv.org/abs/1812.00332)
*accepted at ICLR’19* | - | - |
| [Mixed Precision Quantization of ConvNets via Differentiable Neural Architecture Search(Wu et al. 2018) ](https://arxiv.org/abs/1812.00090) | - | - |
| [Evolving Deep Convolutional Neural Networks by Variable-length Particle Swarm Optimization for Image Classification(Wang et al. 2018) ](https://arxiv.org/abs/1803.06492)
*accepted at CEC’18* | - | - |
| [A Hybrid Differential Evolution Approach to Designing Deep Convolutional Neural Networks for Image Classification(Wang et al. 2018) ](https://arxiv.org/abs/1808.06661)
*accepted at accepted AI’18* | - | - |
| [TEA-DNN: the Quest for Time-Energy-Accuracy Co-optimized Deep Neural Networks(Cai et al. 2018) ](https://arxiv.org/abs/1811.12065) | - | - |
| [Evolving Space-Time Neural Architectures for Videos(Piergiovanni et al. 2018) ](https://arxiv.org/abs/1811.10636)
*accepted at ICCV’19* | Video Models | - |
| [InstaNAS: Instance-aware Neural Architecture Search(Cheng et al. 2018) ](https://arxiv.org/abs/1811.10201) | - | - |
| [Evolutionary-Neural Hybrid Agents for Architecture Search(Maziarz et al. 2018) ](https://arxiv.org/abs/1811.09828)
*accepted at ICML’19 workshop on AutoML* | - | - |
| [Joint Neural Architecture Search and Quantization(Chen et al. 2018) ](https://arxiv.org/abs/1811.09426) | - | - |
| [Transfer Learning with Neural AutoML(Wong et al. 2018) ](http://papers.nips.cc/paper/8056-transfer-learning-with-neural-automl.pdf)
*accepted at NeurIPS’18* | - | - |
| [Evolving Image Classification Architectures with Enhanced Particle Swarm Optimisation(Fielding and Zhang 2018) ](https://ieeexplore.ieee.org/document/8533601) | - | - |
| [Deep Active Learning with a Neural Architecture Search(Geifman and El-Yaniv 2018) ](https://arxiv.org/abs/1811.07579)
*accepted at NeurIPS’19* | - | - |
| [Stochastic Adaptive Neural Architecture Search for Keyword Spotting(Véniat et al. 2018) ](https://arxiv.org/abs/1811.06753) | - | - |
| [NSGA-NET: A Multi-Objective Genetic Algorithm for Neural Architecture Search(Lu et al. 2018) ](https://arxiv.org/abs/1810.03522) | - | - |
| [You only search once: Single Shot Neural Architecture Search via Direct Sparse Optimization(Zhang et al. 2018) ](https://arxiv.org/abs/1811.01567) | - | - |
| [Automatically Evolving CNN Architectures Based on Blocks(Sun et al. 2018) ](https://arxiv.org/abs/1810.11875)
*accepted at accepted by IEEE Transactions on Neural Networks and Learning Systems* | - | - |
| [The CoSTAR Block Stacking Dataset: Learning with Workspace Constraints(Hundt et al. 2018) ](https://arxiv.org/abs/1810.11714)
*accepted at IROS’19* | - | - |
| [Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary Cells(Nekrasov et al. 2018) ](https://arxiv.org/abs/1810.10804)
*accepted at CVPR’19* | Image Segmentation | - |
| [Automatic Configuration of Deep Neural Networks with Parallel Efficient Global Optimization(van Stein et al. 2018) ](https://arxiv.org/abs/1810.05526) | - | - |
| [Gradient Based Evolution to Optimize the Structure of Convolutional Neural Networks(Mitschke et al. 2018) ](https://ieeexplore.ieee.org/document/8451394) | - | - |
| [Searching Toward Pareto-Optimal Device-Aware Neural Architectures(Cheng et al. 2018) ](https://arxiv.org/abs/1808.09830) | - | - |
| [Neural Architecture Optimization(Luo et al. 2018) ](https://arxiv.org/abs/1808.07233)
*accepted at NeurIPS’18* | - | - |
| [Exploring Shared Structures and Hierarchies for Multiple NLP Tasks(Chen et al. 2018) ](https://arxiv.org/abs/1808.07658) | - | - |
| [Neural Architecture Search: A Survey(Elsken et al. 2018) ](https://arxiv.org/abs/1808.05377) | - | - |
| [BlockQNN: Efficient Block-wise Neural Network Architecture Generation(Zhong et al. 2018) ](https://arxiv.org/abs/1808.05584) | - | - |
| [Automatically Designing CNN Architectures Using Genetic Algorithm for Image Classification(Sunet al. 2018) ](https://arxiv.org/abs/1808.03818) | - | - |
| [Reinforced Evolutionary Neural Architecture Search(Chen et al. 2018) ](https://arxiv.org/abs/1808.00193)
*accepted at CVPR’19* | - | - |
| [Teacher Guided Architecture Search(Bashivan et al. 2018) ](https://arxiv.org/abs/1808.01405) | - | - |
| [Efficient Progressive Neural Architecture Search(Perez-Rua et al. 2018) ](https://arxiv.org/abs/1808.00391) | - | - |
| [MnasNet: Platform-Aware Neural Architecture Search for Mobile(Tan et al. 2018) ](https://arxiv.org/abs/1807.11626)
*accepted at CVPR’19* | - | - |
| [Towards Automated Deep Learning: Efficient Joint Neural Architecture and Hyperparameter Search(Zela et al. 2018) ](https://arxiv.org/abs/1807.06906) | - | - |
| [Automatically Designing CNN Architectures for Medical Image Segmentation(Mortazi and Bagci 2018) ](https://arxiv.org/abs/1807.07663) | Medical
Image Segmentation | - |
| [MONAS: Multi-Objective Neural Architecture Search using Reinforcement Learning(Hsu et al. 2018) ](https://arxiv.org/abs/1806.10332) | - | - |
| [Path-Level Network Transformation for Efficient Architecture Search(Cai et al. 2018) ](https://arxiv.org/abs/1806.02639)
*accepted at ICML’18* | - | - |
| [Lamarckian Evolution of Convolutional Neural Networks(Prellberg and Kramer, 2018) ](https://arxiv.org/abs/1806.08099) | - | - |
| [Deep Learning Architecture Search by Neuro-Cell-based Evolution with Function-Preserving Mutations(Wistuba, 2018) ](http://www.ecmlpkdd2018.org/wp-content/uploads/2018/09/108.pdf) | - | - |
| [DARTS: Differentiable Architecture Search(Liu et al. 2018) ](https://arxiv.org/abs/1806.09055)
*accepted at ICLR’19* | - | - |
| [Constructing Deep Neural Networks by Bayesian Network Structure Learning(Rohekar et al. 2018) ](https://arxiv.org/abs/1806.09141) | - | - |
| [Resource-Efficient Neural Architect(Zhou et al. 2018) ](https://arxiv.org/abs/1806.07912) | - | - |
| [Efficient Neural Architecture Search with Network Morphism(Jin et al. 2018) ](https://arxiv.org/abs/1806.10282) | - | - |
| [TAPAS: Train-less Accuracy Predictor for Architecture Search(Istrate et al. 2018) ](https://arxiv.org/abs/1806.00250) | - | - |
| [Neural Architecture Search using Deep Neural Networks and Monte Carlo Tree Search(Wang et al 2018) ](https://arxiv.org/abs/1805.07440)
*accepted at AAAI’20* | - | - |
| [Multi-objective Architecture Search for CNNs(Elsken et al. 2018) ](https://arxiv.org/abs/1804.09081) | - | - |
| [GNAS: A Greedy Neural Architecture Search Method for Multi-Attribute Learning(Huang et al 2018) ](https://arxiv.org/abs/1804.06964) | - | - |
| [Evolutionary Architecture Search For Deep Multitask Networks(Liang et al. 2018) ](https://arxiv.org/abs/1803.03745) | - | - |
| [From Nodes to Networks: Evolving Recurrent Neural Networks(Rawal et al. 2018) ](https://arxiv.org/abs/1803.04439) | - | - |
| [Neural Architecture Construction using EnvelopeNets(Kamath et al. 2018) ](https://arxiv.org/abs/1803.06744) | - | - |
| [Transfer Automatic Machine Learning(Wong et al. 2018) ](https://arxiv.org/abs/1803.02780) | - | - |
| [Neural Architecture Search with Bayesian Optimisation and Optimal Transport(Kandasamy et al. 2018) ](https://arxiv.org/abs/1802.07191) | - | - |
| [Efficient Neural Architecture Search via Parameter Sharing(Pham et al. 2018) ](https://arxiv.org/abs/1802.03268)
*accepted at ICML’18* | - | - |
| [Regularized Evolution for Image Classifier Architecture Search(Real et al. 2018) ](https://arxiv.org/abs/1802.01548) | - | - |
| [Effective Building Block Design for Deep Convolutional Neural Networks using Search(Dutta et al. 2018) ](https://arxiv.org/abs/1801.08577) | - | - |
| [Combination of Hyperband and Bayesian Optimization for Hyperparameter Optimization in Deep Learning(Wang et al. 2018) ](https://arxiv.org/abs/1801.01596) | - | - |
| [Memetic Evolution of Deep Neural Networks(Lorenzo and Nalepa 2018) ](https://dl.acm.org/citation.cfm?id=3205631) | - | - |
| [Understanding and Simplifying One-Shot Architecture Search(Bender et al. 2018) ](http://proceedings.mlr.press/v80/bender18a/bender18a.pdf)
*accepted at ICML’18* | - | - |
| [Differentiable Neural Network Architecture Search(Shin et al. 2018) ](https://openreview.net/pdf?id=BJ-MRKkwG)
*accepted at ICLR’18 workshop* | - | - |
| [PPP-Net: Platform-aware progressive search for pareto-optimal neural architectures(Dong et al. 2018) ](https://openreview.net/pdf?id=B1NT3TAIM)
*accepted at ICLR’18 workshop* | - | - |
| [Speeding up the Hyperparameter Optimization of Deep Convolutional Neural Networks(Hinz et al. 2018) ](https://www.worldscientific.com/doi/abs/10.1142/S1469026818500086) | - | - |
| [Gitgraph – From Computational Subgraphs to Smaller Architecture search spaces(Bennani-Smires et al. 2018) ](https://openreview.net/pdf?id=rkiO1_1Pz) | - | - |
### 2017
- [back to top](#2020)
| Title | Tags | Code |
|:--------|:--------:|:--------:|
| [N2N Learning: Network to Network Compression via Policy Gradient Reinforcement Learning(Ashok et al. 2017) ](https://arxiv.org/abs/1709.06030)
*accepted at ICLR’18* | - | - |
| [Genetic CNN(Xie and Yuille, 2017) ](https://arxiv.org/abs/1703.01513)
*accepted at ICCV’17* | - | - |
| [MorphNet: Fast & Simple Resource-Constrained Structure Learning of Deep Networks(Gordon et al. 2017) ](https://arxiv.org/abs/1711.06798) | - | - |
| [MaskConnect: Connectivity Learning by Gradient Descent(Ahmed and Torresani. 2017) ](https://arxiv.org/abs/1709.09582)
*accepted at ECCV’18* | - | - |
| [A Flexible Approach to Automated RNN Architecture Generation(Schrimpf et al. 2017) ](https://arxiv.org/abs/1712.07316) | - | - |
| [DeepArchitect: Automatically Designing and Training Deep Architectures(Negrinho and Gordon 2017) ](https://arxiv.org/abs/1704.08792) | - | - |
| [A Genetic Programming Approach to Designing Convolutional Neural Network Architectures(Suganuma et al. 2017) ](https://arxiv.org/abs/1704.00764)
*accepted at GECCO’17* | - | - |
| [Practical Block-wise Neural Network Architecture Generation(Zhong et al. 2017) ](https://arxiv.org/abs/1708.05552)
*accepted at CVPR’18* | - | - |
| [Accelerating Neural Architecture Search using Performance Prediction(Baker et al. 2017) ](https://arxiv.org/abs/1705.10823)
*accepted at NeurIPS worshop on Meta-Learning 2017* | - | - |
| [Large-Scale Evolution of Image Classifiers(Real et al. 2017) ](https://arxiv.org/abs/1703.01041)
*accepted at ICML’17* | - | - |
| [Hierarchical Representations for Efficient Architecture Search(Liu et al. 2017) ](https://arxiv.org/abs/1711.00436)
*accepted at ICLR’18* | - | - |
| [Neural Optimizer Search with Reinforcement Learning(Bello et al. 2017) ](https://arxiv.org/abs/1709.07417) | - | - |
| [Progressive Neural Architecture Search(Liu et al. 2017) ](https://arxiv.org/abs/1712.00559)
*accepted at ECCV’18* | - | - |
| [Learning Transferable Architectures for Scalable Image Recognition(Zoph et al. 2017) ](https://arxiv.org/abs/1707.07012)
*accepted at CVPR’18* | - | - |
| [Simple And Efficient Architecture Search for Convolutional Neural Networks(Elsken et al. 2017) ](https://arxiv.org/abs/1711.04528)
*accepted at NeurIPS workshop on Meta-Learning’17* | - | - |
| [Bayesian Optimization Combined with Incremental Evaluation for Neural Network Architecture Optimization(Wistuba, 2017) ](https://www.semanticscholar.org/paper/Bayesian-Optimization-Combined-with-Successive-for-Wistuba/ddb182533c91f0941f088e1e298c52a111253554) | - | - |
| [Finding Competitive Network Architectures Within a Day Using UCT(Wistuba 2017) ](https://arxiv.org/abs/1712.07420) | - | - |
| [Hyperparameter Optimization: A Spectral Approach(Hazan et al. 2017) ](https://arxiv.org/abs/1706.00764) | - | - |
| [SMASH: One-Shot Model Architecture Search through HyperNetworks(Brock et al. 2017) ](https://arxiv.org/abs/1708.05344)
*accepted at NeurIPS workshop on Meta-Learning’17* | - | - |
| [Efficient Architecture Search by Network Transformation(Cai et al. 2017) ](https://arxiv.org/abs/1707.04873)
*accepted at AAAI’18* | - | - |
| [Modularized Morphing of Neural Networks(Wei et al. 2017) ](https://arxiv.org/abs/1701.03281) | - | - |
### 2016
- [back to top](#2020)
| Title | Tags | Code |
|:--------|:--------:|:--------:|
| [Towards Automatically-Tuned Neural Networks(Mendoza et al. 2016) ](http://proceedings.mlr.press/v64/mendoza_towards_2016.html)
*accepted at ICML AutoML workshop* | - | - |
| [Neural Networks Designing Neural Networks: Multi-Objective Hyper-Parameter Optimization(Smithson et al. 2016) ](https://arxiv.org/abs/1611.02120) | - | - |
| [AdaNet: Adaptive Structural Learning of Artificial Neural Networks(Cortes et al. 2016) ](https://arxiv.org/abs/1607.01097) | - | - |
| [Network Morphism(Wei et al. 2016) ](https://arxiv.org/abs/1603.01670) | - | - |
| [Convolutional Neural Fabrics(Saxena and Verbeek 2016) ](https://arxiv.org/abs/1606.02492)
*accepted at NeurIPS’16* | - | - |
| [CMA-ES for Hyperparameter Optimization of Deep Neural Networks(Loshchilov and Hutter 2016) ](https://arxiv.org/abs/1604.07269) | - | - |
| [Designing Neural Network Architectures using Reinforcement Learning(Baker et al. 2016) ](https://arxiv.org/abs/1611.02167)
*accepted at ICLR’17* | - | - |
| [Neural Architecture Search with Reinforcement Learning(Zoph and Le. 2016) ](https://arxiv.org/abs/1611.01578)
*accepted at ICLR’17* | - | - |
| [Learning curve prediction with Bayesian Neural Networks(Klein et al. 2017: accepted at ICLR’17) ](http://ml.informatik.uni-freiburg.de/papers/17-ICLR-LCNet.pdf) | - | - |
| [Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization(Li et al. 2016) ](https://arxiv.org/abs/1603.06560) | - | - |
### 1988-2015
- [back to top](#2020)
| Title | Tags | Code |
|:--------|:--------:|:--------:|
| [Net2Net: Accelerating Learning via Knowledge Transfer(Chen et al. 2015) ](https://arxiv.org/abs/1511.05641)
*accepted at ICLR’16* | - | - |
| [Optimizing deep learning hyper-parameters through an evolutionary algorithm(Young et al. 2015) ](https://dl.acm.org/citation.cfm?id=2834896) | - | - |
| [Practical Bayesian Optimization of Machine Learning Algorithms(Snoek et al. 2012) ](https://papers.nips.cc/paper/4522-practical-bayesian-optimization-of-machine-learning-algorithms.pdf)
*accepted at NeurIPS’12* | - | - |
| [A Hypercube-based Encoding for Evolving large-scale Neural Networks(Stanley et al. 2009) ](https://ieeexplore.ieee.org/document/6792316/) | - | - |
| [Neuroevolution: From Architectures to Learning(Floreano et al. 2008) ](https://link.springer.com/article/10.1007/s12065-007-0002-4)
*accepted at Evolutionary Intelligence’08* | - | - |
| [Evolving Neural Networks through Augmenting Topologies(Stanley and Miikkulainen, 2002) ](http://nn.cs.utexas.edu/downloads/papers/stanley.ec02.pdf)
*accepted at Evolutionary Computation’02* | - | - |
| [Evolving Artificial Neural Networks(Yao, 1999) ](https://ieeexplore.ieee.org/document/784219/)
*accepted at IEEE* | - | - |
| [An Evolutionary Algorithm that Constructs Recurrent Neural Networks(Angeline et al. 1994) ](https://ieeexplore.ieee.org/document/265960/) | - | - |
| [Designing Neural Networks Using Genetic Algorithms with Graph Generation System(Kitano, 1990) ](http://www.complex-systems.com/abstracts/v04_i04_a06/) | - | - |
| [Designing Neural Networks using Genetic Algorithms(Miller et al. 1989) ](https://dl.acm.org/citation.cfm?id=94034)
*accepted at ICGA’89* | - | - |
| [The Cascade-Correlation Learning Architecture(Fahlman and Leblere, 1989) ](https://papers.nips.cc/paper/207-the-cascade-correlation-learning-architecture)
*accepted at NeurIPS’89* | - | - |
| [Self Organizing Neural Networks for the Identification Problem(Tenorio and Lee, 1988) ](https://papers.nips.cc/paper/149-self-organizing-neural-networks-for-the-identification-problem)
*accepted at NeurIPS’88* | - | - |