https://github.com/sk-g/readings
A collection of some nice papers/research articles.
https://github.com/sk-g/readings
adversarial-networks cnn deep-learning gan machine-learning nips nips-2017 object-recognition papers research rnn tensorflow tensorflow-experiments
Last synced: 3 months ago
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A collection of some nice papers/research articles.
- Host: GitHub
- URL: https://github.com/sk-g/readings
- Owner: sk-g
- Created: 2018-03-17T18:18:18.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2018-07-02T20:11:19.000Z (over 7 years ago)
- Last Synced: 2025-02-09T01:18:22.128Z (8 months ago)
- Topics: adversarial-networks, cnn, deep-learning, gan, machine-learning, nips, nips-2017, object-recognition, papers, research, rnn, tensorflow, tensorflow-experiments
- Size: 19.5 KB
- Stars: 0
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: ReadMe.md
Awesome Lists containing this project
README
Jump To -
[CNNs](https://github.com/sk-g/readings#convolutional-neural-networks)
[Blogs,etc](https://github.com/sk-g/readings#interesting-blog-posts-and-quora-answers)
[GANs](https://github.com/sk-g/readings#generative-adversarial-networks)
[NLP](https://github.com/sk-g/readings#NLP/Language-Modeling-with-NNs)
[Reinforcement Learning](https://github.com/sk-g/readings#reinforcement-learning)
[Attacks on ML Models](https://github.com/sk-g/readings#adversarial-attacks)
[Privacy in ML](https://github.com/sk-g/readings/blob/master/ReadMe.md#privacy-in-machine-learning)
## Convolutional Neural Networks
[Dynamic Routing Between Capsules](https://papers.nips.cc/paper/6975-dynamic-routing-between-capsules.pdf)
[How to double dip into your holdout set](https://www.zillow.com/data-science/double-dip-holdout-set/)
[MMdnn - Microsoft framework for converting models from framework to framework](https://github.com/Microsoft/MMdnn "Code")
[Variational Dropout Sparsifies Deep Neural Networks](https://arxiv.org/abs/1701.05369 "Paper") [Code](https://github.com/ars-ashuha/variational-dropout-sparsifies-dnn "Code")
[Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://arxiv.org/pdf/1506.01497)
[Going Deeper with Convolutions](https://www.cs.unc.edu/~wliu/papers/GoogLeNet.pdf)
[Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://arxiv.org/abs/1502.03167)
[Rethinking the Inception Architecture for Computer Vision](https://arxiv.org/abs/1512.00567)
## Interesting Blog Posts and Quora Answers
[Colah's Visualizing MNIST](http://colah.github.io/posts/2014-10-Visualizing-MNIST/)
[Great Blog Post about GANS by Nvidia](https://blogs.nvidia.com/blog/2017/05/17/generative-adversarial-network/)
## Generative Adversarial Networks
[Super Awesome list of GAN papers and their code](https://github.com/zhangqianhui/AdversarialNetsPapers "Adversarial Nets Papers")
[A tutorial on Energy based Learning](https://scholar.google.com/citations?citation_for_view=WLN3QrAAAAAJ%3A8k81kl-MbHgC&cstart=20&hl=en&pagesize=80&user=WLN3QrAAAAAJ&view_op=view_citation "Yann LeCun")
[Generative Visual Manipulation on the Natural Image Manifold](http://people.eecs.berkeley.edu/~junyanz/projects/gvm/)
[Connecting Generative Adversarial Networks and Actor-Critic Methods](https://arxiv.org/pdf/1610.01945.pdf)
[Wasserstein GAN](https://arxiv.org/abs/1701.07875 "Facebook")
[Learning from Simulated and Unsupervised Images through Adversarial Training](https://arxiv.org/pdf/1612.07828.pdf "Apple Inc")
[Adversarial Feature Matching for Text Generation](https://arxiv.org/pdf/1706.03850.pdf "Paper") [Code](https://github.com/dreasysnail/textGAN_public "Code")
[Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks](https://arxiv.org/abs/1703.10593 "Paper")[PyTorch Code](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix "Code")
## Reinforcement Learning
[Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning](https://arxiv.org/abs/1712.06567)
[Evolution Strategies as a Scalable Alternative to Reinforcement Learning](https://arxiv.org/pdf/1703.03864.pdf)
## Adversarial Attacks
[Adversarial Patch](https://arxiv.org/pdf/1712.09665.pdf)
[CleverHans, benchmarking tool for ML model robustness against adversarial attacks](https://github.com/tensorflow/cleverhans "Code")
## Privacy in Machine Learning
[Deep Learning With Differential Privacy](https://arxiv.org/pdf/1607.00133.pdf)
[Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data](https://arxiv.org/pdf/1610.05755.pdf)
[Membership Inference Attacks Against Machine Learning Models](https://arxiv.org/pdf/1610.05820.pdf)
[The reusable holdout: Preserving validity in adaptive data analysis](https://pdfs.semanticscholar.org/25fe/96591144f4af3d8f8f79c95b37f415e5bb75.pdf)
[Scalable Private Learning with PATE](https://openreview.net/forum?id=rkZB1XbRZ)
[Eye In-Painting with Exemplar Generative Adversarial Networks] [[Paper]](https://arxiv.org/abs/1712.03999)[[Introduction]](hhttps://github.com/bdol/exemplar_gans)[[Tensorflow code]](https://github.com/zhangqianhui/Exemplar_GAN_Eye_Inpainting)(CVPR2018)
[Eye In-Painting with Exemplar Generative Adversarial Networks] [[Paper]](https://arxiv.org/abs/1712.03999)[[Introduction]](https://github.com/bdol/exemplar_gans)[[Tensorflow code]](https://github.com/zhangqianhui/Exemplar_GAN_Eye_Inpainting)(CVPR2018)
[Generative Image Inpainting with Contextual Attention] [[Paper]](https://arxiv.org/abs/1801.07892)[[Project]](http://jiahuiyu.com/deepfill)[[Demo]](http://jiahuiyu.com/deepfill)[[YouTube]](https://youtu.be/xz1ZvcdhgQ0)[[Code]](https://github.com/JiahuiYu/generative_inpainting)(CVPR2018)
## NLP/Language Modeling with NNs
[Fast Text](https://fasttext.cc/) [Introduction](https://research.fb.com/fasttext/ "Facebook Research") [Code](https://github.com/facebookresearch/fastText)
[UNSUPERVISED NEURAL MACHINE TRANSLATION](https://arxiv.org/pdf/1710.11041.pdf "paper") [Code](https://github.com/artetxem/undreamt "Code")
[SUBWORD LANGUAGE MODELING WITH NEURAL NETWORKS](http://www.fit.vutbr.cz/~imikolov/rnnlm/char.pdf "paper")
[Data Noising as Smoothing in Neural Network Language Models](https://arxiv.org/pdf/1703.02573 "paper")
[Highway Networks](https://arxiv.org/pdf/1505.00387 "paper")