Ecosyste.ms: Awesome

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

Awesome Lists | Featured Topics | Projects

https://github.com/leo-p/papers

Papers and their summary (in issue)
https://github.com/leo-p/papers

Last synced: 7 days ago
JSON representation

Papers and their summary (in issue)

Awesome Lists containing this project

README

        

# Papers read so far
- 17.06 - **Self-Normalizing Neural Networks**
- [pdf](https://arxiv.org/pdf/1706.02515), [summary](https://github.com/leo-p/papers/issues/31)
- 17.06 - **StreetStyle: Exploring world-wide clothing styles from millions of photos**
- [pdf](https://arxiv.org/pdf/1706.01869), [summary](https://github.com/leo-p/papers/issues/35)
- 17.05 - **Visual Attribute Transfer through Deep Image Analogy**
- [pdf](https://arxiv.org/pdf/1705.01088), [summary](https://github.com/leo-p/papers/issues/24)
- 17.05 - **pix2code: Generating Code from a Graphical User Interface Screenshot**
- [pdf](https://arxiv.org/pdf/1705.07962), [summary](https://github.com/leo-p/papers/issues/36)
- 17.05 - **A System for Accessible Artificial Intelligence**
- [pdf](https://arxiv.org/pdf/1705.00594), [summary](https://github.com/leo-p/papers/issues/39)
- 17.05 - **Domain Adaptation with Randomized Multilinear Adversarial Networks**
- [pdf](https://arxiv.org/pdf/1705.10667), [summary](https://github.com/leo-p/papers/issues/29)
- 17.04 - **Softmax GAN**
- [pdf](https://arxiv.org/pdf/1704.06191), [summary](https://github.com/leo-p/papers/issues/23)
- 17.04 - **Generate To Adapt - Aligning Domains using Generative Adversarial Networks**
- [pdf](https://arxiv.org/pdf/1704.01705v1), [summary](https://github.com/leo-p/papers/issues/22)
- 17.03 - **Neural Episodic Control**
- [pdf](https://arxiv.org/pdf/1703.01988), [summary](https://github.com/leo-p/papers/issues/21)
- 17.03 - **Mask R-CNN**
- [pdf](https://arxiv.org/pdf/1703.06870), [summary](https://github.com/leo-p/papers/issues/20)
- 17.03 - **BEGAN- Boundary Equilibrium Generative Adversarial Networks**
- [pdf](https://arxiv.org/pdf/1703.10717v3), [summary](https://github.com/leo-p/papers/issues/19)
- 17.03 - **Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks**
- [pdf](https://arxiv.org/pdf/1703.10593), [summary](https://github.com/leo-p/papers/issues/30)
- 17.02 - **Understanding Deep Learning Requires Rethinking Generalization**
- [pdf](https://arxiv.org/pdf/1611.03530), [summary](https://github.com/leo-p/papers/issues/18)
- 17.02 - **Adversarial Discriminative Domain Adaptation**
- [pdf](https://arxiv.org/pdf/1702.05464), [summary](https://github.com/leo-p/papers/issues/17)
- 17.01 - **Cost-Effective Active Learning for Deep Image Classification**
- [pdf](https://arxiv.org/pdf/1701.03551), [summary](https://github.com/leo-p/papers/issues/40)
- 17.01 - **Wasserstein GAN**
- [pdf](https://arxiv.org/pdf/1701.07875), [summary](https://github.com/leo-p/papers/issues/16)
- 17.01 - **NIPS Tutorial - Generative Adversarial Networks**
- [pdf](https://arxiv.org/pdf/1701.00160), [summary](https://github.com/leo-p/papers/issues/15)
- 17.01 - **Learning From Noisy Large-Scale Datasets With Minimal Supervision**
- [pdf](https://arxiv.org/pdf/1701.01619), [summary](https://github.com/leo-p/papers/issues/14)
- 16.12 - **Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space**
- [pdf](https://arxiv.org/pdf/1612.00005v2), [summary](https://github.com/leo-p/papers/issues/28)
- 16.12 - **YOLO9000: Better, Faster, Stronger**
- [pdf](https://arxiv.org/pdf/1612.08242), [summary](https://github.com/leo-p/papers/issues/13)
- 16.11 - **Deep Information Propagation**
- [pdf](https://arxiv.org/pdf/1611.01232), [summary](https://github.com/leo-p/papers/issues/12)
- 16.11 - **Speed/accuracy trade-offs for modern convolutional object detectors**
- [pdf](https://arxiv.org/pdf/1611.10012), [summary](https://github.com/leo-p/papers/issues/37)
- 16/11 - **Neural Architecture Search with Reinforcement Learning**
- [pdf](https://arxiv.org/pdf/1611.01578), [summary](https://github.com/leo-p/papers/issues/26)
- 16.07 - **Deep Reconstruction-Classification Networks for Unsupervised Domain Adaptation**
- [pdf](https://arxiv.org/pdf/1607.03516), [summary](https://github.com/leo-p/papers/issues/11)
- 16.05 - **Domain-Adversarial Training of Neural Networks**
- [pdf](https://arxiv.org/pdf/1505.07818), [summary](https://github.com/leo-p/papers/issues/6)
- 16.01 - **Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks**
- [pdf](https://arxiv.org/pdf/1506.01497), [summary](https://github.com/leo-p/papers/issues/9)
- 15.12 - **Deep Residual Learning for Image Recognition**
- [pdf](https://arxiv.org/pdf/1512.03385), [summary](https://github.com/leo-p/papers/issues/27)
- 15.11 - **Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks**
- [pdf](https://arxiv.org/pdf/1511.06434), [summary](https://github.com/leo-p/papers/issues/8)
- 15.04 - **Active Learning by Learning**
- [pdf](https://www.csie.ntu.edu.tw/~htlin/paper/doc/aaai15albl.pdf), [summary](https://github.com/leo-p/papers/issues/34)
- 15.04 - **Fast R-CNN**
- [pdf](https://arxiv.org/pdf/1504.08083), [summary](https://github.com/leo-p/papers/issues/7)
- 15.02 - **Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift**
- [pdf](https://arxiv.org/pdf/1502.03167), [summary](https://github.com/leo-p/papers/issues/5)
- 14.12 - **Dropout: A Simple Way to Prevent Neural Networks from Overfitting**
- [pdf](https://www.cs.toronto.edu/~hinton/absps/JMLRdropout.pdf), [summary](test)
- 14.12 - **Training Deep Neural Networks on Noisy Labels with Bootstrapping**
- [pdf](https://arxiv.org/pdf/1412.6596), [summary](https://github.com/leo-p/papers/issues/38)
- 14.11 - **Conditional Generative Adversarial Nets**
- [pdf](https://arxiv.org/pdf/1411.1784), [summary](https://github.com/leo-p/papers/issues/4)
- 14.09 - **Unsupervised Domain Adaptation by Backpropagation**
- [pdf](https://arxiv.org/pdf/1409.7495), [summary](https://github.com/leo-p/papers/issues/3)
- 14.06 - **Generative Adversarial Nets**
- [pdf](https://arxiv.org/pdf/1406.2661), [summary](https://github.com/leo-p/papers/issues/2)
- 14.06 - **Learning and Transferring Mid-Level Image Representations using Convolutional Neural Networks**
- [pdf](https://www.di.ens.fr/willow/pdfscurrent/oquab14cvpr.pdf), [summary](https://github.com/leo-p/papers/issues/32)
- 11.06 - **Stacked Convolutional Auto-Encoders for Hierarchical Feature Extraction**
- [pdf](https://pdfs.semanticscholar.org/1c6d/990c80e60aa0b0059415444cdf94b3574f0f.pdf), [summary](https://github.com/leo-p/papers/issues/25)
- 10.06 - **Deconvolutional Networks**
- [pdf](http://www.matthewzeiler.com/pubs/cvpr2010/cvpr2010.pdf), [summary](https://github.com/leo-p/papers/issues/1)
- 10.01 - **Active Learning Literature Survey**
- [pdf](http://burrsettles.com/pub/settles.activelearning.pdf), [summary](https://github.com/leo-p/papers/issues/33)