Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/ThyrixYang/awesome-artificial-intelligence-research

A curated list of Artificial Intelligence (AI) Research, tracks the cutting edge trending of AI research, including recommender systems, computer vision, machine learning, etc.
https://github.com/ThyrixYang/awesome-artificial-intelligence-research

List: awesome-artificial-intelligence-research

artificial-intelligence automl awesome-list computer-vision deep-learning detection domain-adaptation generative-adversarial-network graph-neural-networks incremental-learning interpretability machine-learning meta-learning natural-language-processing paper recognition reinforcement-learning reinforcement-learning-resources self-supervised-learning transfer-learning

Last synced: 16 days ago
JSON representation

A curated list of Artificial Intelligence (AI) Research, tracks the cutting edge trending of AI research, including recommender systems, computer vision, machine learning, etc.

Host: GitHub
URL: https://github.com/ThyrixYang/awesome-artificial-intelligence-research
Owner: ThyrixYang
Created: 2020-10-17T05:58:31.000Z (about 4 years ago)
Default Branch: main
Last Pushed: 2022-11-28T15:15:30.000Z (about 2 years ago)
Last Synced: 2024-05-20T05:31:40.153Z (7 months ago)
Topics: artificial-intelligence, automl, awesome-list, computer-vision, deep-learning, detection, domain-adaptation, generative-adversarial-network, graph-neural-networks, incremental-learning, interpretability, machine-learning, meta-learning, natural-language-processing, paper, recognition, reinforcement-learning, reinforcement-learning-resources, self-supervised-learning, transfer-learning
Homepage:
Size: 44.9 KB
Stars: 113
Watchers: 5
Forks: 15
Open Issues: 0
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml

Awesome Lists containing this project

ultimate-awesome - awesome-artificial-intelligence-research - A curated list of Artificial Intelligence (AI) Research, tracks the cutting edge trending of AI research, including recommender systems, computer vision, machine learning, etc. (Other Lists / Monkey C Lists)

README

        # Awesome Artificial Intelligence (AI) Research [![Awesome](https://awesome.re/badge.svg)](https://awesome.re)

Artificial Intelligence (AI) has become a vast research area, with tens of thousands of papers published every year. It's more and more difficult to track a specific topic among many top conferences and journals. This list aims to maintain a meta list of up-to-date research paper lists to help researchers get familiar with cutting edge research in a specific area, and (hopefully) provide a large scale picture of the trending of AI research.

## Machine Learning (ML) and Data Mining (DM)

* [AutoML](https://github.com/hibayesian/awesome-automl-papers)

* [Adversarial Machine Learning (AML)](https://github.com/wangjksjtu/awesome-AML)

* [Automated Deep Learning](https://github.com/D-X-Y/Awesome-AutoDL)

* [Bayesian Deep Learning](https://github.com/js05212/BayesianDeepLearning-Survey)

* [Causality](https://github.com/rguo12/awesome-causality-algorithms)

* [Domain Adaptation](https://github.com/zhaoxin94/awesome-domain-adaptation)

* [Decision Tree](https://github.com/benedekrozemberczki/awesome-decision-tree-papers)

* Federated Learning

  * [Federated Learning](https://github.com/weimingwill/awesome-federated-learning)

  * [Federated Machine Learning](https://github.com/innovation-cat/Awesome-Federated-Machine-Learning)

* [Generative Adversarial (Neural) Networks](https://github.com/nightrome/really-awesome-gan#papers)

* Graph / Network Learning

  * [Community Detection](https://github.com/benedekrozemberczki/awesome-community-detection)

  * [Graph Adversarial Learning](https://github.com/safe-graph/graph-adversarial-learning-literature)

  * [Graph Neural Networks](https://github.com/nnzhan/Awesome-Graph-Neural-Networks)

  * [Graph Classification](https://github.com/benedekrozemberczki/awesome-graph-classification)

  * [Graph Based Deep Learning](https://github.com/naganandy/graph-based-deep-learning-literature)

  * [Knowledge Graph](https://github.com/shaoxiongji/knowledge-graphs)

  * [Knowledge Representation Learning](https://github.com/thunlp/KRLPapers)

  * [Network Embedding](https://github.com/chihming/awesome-network-embedding)

  * [Network Representation Learning](https://github.com/thunlp/NRLPapers)

  * [Must-read papers on GNN](https://github.com/thunlp/GNNPapers)

  * [Self-Supervised-GNN](https://github.com/ChandlerBang/awesome-self-supervised-gnn)

* Incremental Learning / Continual Learning / Lifelong Learning

  * [Incremental Learning / Lifelong Learning](https://github.com/xialeiliu/Awesome-Incremental-Learning)

  * [Continual Learning](https://github.com/optimass/continual_learning_papers)

* [Interpretability](https://github.com/jphall663/awesome-machine-learning-interpretability#review-and-general-papers)

* [Interpretability 2](https://github.com/lopusz/awesome-interpretable-machine-learning)

* [Interpretability & Explainable-AI](https://github.com/wangyongjie-ntu/Awesome-explainable-AI)

* [Knowledge Distillation](https://github.com/dkozlov/awesome-knowledge-distillation)

* [Learning with Label Noise](https://github.com/subeeshvasu/Awesome-Learning-with-Label-Noise)

* [Meta Learning](https://github.com/sudharsan13296/Awesome-Meta-Learning)

* [Multimodal Machine Learning](https://github.com/pliang279/awesome-multimodal-ml)

* [Neural Architecture Search](https://github.com/markdtw/awesome-architecture-search)

* [Neural Ordinary Differential Equations(ODE)](https://github.com/Zymrael/awesome-neural-ode)

* [Normalizing Flows](https://github.com/janosh/awesome-normalizing-flows)

* [Partial Label Learning](https://github.com/wwangwitsel/awesome-partial-label-learning)

* [Reasoning](https://github.com/floodsung/Deep-Reasoning-Papers)

* [Reinforcement Learning](https://github.com/aikorea/awesome-rl)

* [Open World Learning](https://github.com/zhoudw-zdw/Awesome-open-world-learning)

* [Online Learning](https://github.com/MaxHalford/awesome-online-machine-learning)

* [Reinforcement Learning 2](https://github.com/tigerneil/awesome-deep-rl)

* Self Supervised Learning

  * [Self-Supervised Learning](https://github.com/jason718/awesome-self-supervised-learning)

  * [Self-Supervised-GNN](https://github.com/ChandlerBang/awesome-self-supervised-gnn)

* [Transfer Learning](https://github.com/artix41/awesome-transfer-learning)

* [Uncertainty](https://github.com/ahmedmalaa/deep-learning-uncertainty)

* [Machine Learning Surveys (did not update since 2017)](https://github.com/metrofun/machine-learning-surveys)

* [Deep Learning (did not update since 2016)](https://github.com/terryum/awesome-deep-learning-papers)

## Applications

### Computer Vision (CV)

* [Action Recognition](https://github.com/jinwchoi/awesome-action-recognition)

* [Deblurring](https://github.com/subeeshvasu/Awesome-Deblurring)

* [Denoising](https://github.com/oneTaken/Awesome-Denoise)

* [Face Recognition](https://github.com/ChanChiChoi/awesome-Face_Recognition)

* Object Detection

  * [Object Detection](https://github.com/amusi/awesome-object-detection)

  * [Object Detection 2](https://github.com/hoya012/deep_learning_object_detection)

  * [Aerial Object Detection](https://github.com/murari023/awesome-aerial-object-detection)

  * [Tiny Object Detection](https://github.com/kuanhungchen/awesome-tiny-object-detection)

  * [Lane Detection](https://github.com/amusi/awesome-lane-detection)

* [Hand Pose Estimation](https://github.com/xinghaochen/awesome-hand-pose-estimation)

* [Human Pose Estimation](https://github.com/cbsudux/awesome-human-pose-estimation)

* [Image Classification](https://github.com/weiaicunzai/awesome-image-classification)

* [Image Retrieval](https://github.com/willard-yuan/awesome-cbir-papers)

* [Medical Image Synthesis](https://github.com/xinario/awesome-gan-for-medical-imaging)

* [Multi Object Tracking](https://github.com/SpyderXu/multi-object-tracking-paper-list)

* Point Cloud

  * [Point Cloud Analysis](https://github.com/NUAAXQ/awesome-point-cloud-analysis-2021)

  * [Point Cloud](https://github.com/zhulf0804/3D-PointCloud)

* [Scene Text Localization & Recognition](https://github.com/chongyangtao/Awesome-Scene-Text-Recognition)

* [Temporal Action Localization](https://github.com/Alvin-Zeng/Awesome-Temporal-Action-Localization)

* [Text Detection Recognition](https://github.com/hwalsuklee/awesome-deep-text-detection-recognition)

* [Transformer Architectures](https://github.com/dk-liang/Awesome-Visual-Transformer)

* [Visual Grounding](https://github.com/TheShadow29/awesome-grounding)

* [Awesome Deep Vision (did not update since 2016)](https://github.com/kjw0612/awesome-deep-vision)

### Natural Language Processing (NLP)

* [Natural Language Generation](https://github.com/tokenmill/awesome-nlg)

* [Sentence Embedding](https://github.com/Separius/awesome-sentence-embedding)

### Audio & Speech

* [Speech Enhancement](https://github.com/nanahou/Awesome-Speech-Enhancement)

* [Speech Recognition & Speech Synthesis](https://github.com/zzw922cn/awesome-speech-recognition-speech-synthesis-papers)

* [Speech Synthesis](https://github.com/xcmyz/speech-synthesis-paper)

### Other Applications

* [Anomaly Detection](https://github.com/yzhao062/anomaly-detection-resources#4-papers)

* [Anomaly Detection 2](https://github.com/hoya012/awesome-anomaly-detection)

* [AI Security](https://github.com/DeepSpaceHarbor/Awesome-AI-Security)

* [Computational Biology](https://github.com/gokceneraslan/awesome-deepbio)

* [Crowd Counting](https://github.com/gjy3035/Awesome-Crowd-Counting)

* [Cyber Security](https://github.com/jivoi/awesome-ml-for-cybersecurity)

* [Database Learning](https://github.com/pingcap/awesome-database-learning)

* [Deep Learning for Music (DL4M)](https://github.com/ybayle/awesome-deep-learning-music)

* Recommender Systems

  * [Recommender System](https://github.com/scnu-dil/awesome-RecSys)

  * [Recommender System 2](https://github.com/hongleizhang/RSPapers)

  * [Real Time Bidding](https://github.com/wnzhang/rtb-papers)

  * [Search Recommendation Advertising](https://github.com/guyulongcs/Awesome-Deep-Learning-Papers-for-Search-Recommendation-Advertising)

* [Search \& Recommendation Advertising](https://github.com/guyulongcs/Awesome-Deep-Learning-Papers-for-Search-Recommendation-Advertising)

* [Quant Machine Learning Trading](https://github.com/grananqvist/Awesome-Quant-Machine-Learning-Trading)

* [Threat Detection and Hunting](https://github.com/0x4D31/awesome-threat-detection#research-papers)

* [Autonomous Vehicles (did not update since 2016)](https://github.com/manfreddiaz/awesome-autonomous-vehicles#papers)

## New Paper Recommendation

* [Awesome NLP Paper Discussions (by The Hugging Face team)](https://github.com/huggingface/awesome-papers)

## Surveys

* [Machine Learning Surveys](https://github.com/eugeneyan/ml-surveys)

## Tools For Research

* [Awesome Public Datasets](https://github.com/awesomedata/awesome-public-datasets)