{"id":23075085,"url":"https://github.com/sk-g/readings","last_synced_at":"2025-07-24T11:43:17.527Z","repository":{"id":80507149,"uuid":"125657057","full_name":"sk-g/readings","owner":"sk-g","description":"A collection of some nice papers/research 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To - \r\n\r\n[CNNs](https://github.com/sk-g/readings#convolutional-neural-networks)\r\n\r\n[Blogs,etc](https://github.com/sk-g/readings#interesting-blog-posts-and-quora-answers)\r\n\r\n[GANs](https://github.com/sk-g/readings#generative-adversarial-networks)\r\n\r\n[NLP](https://github.com/sk-g/readings#NLP/Language-Modeling-with-NNs)\r\n\r\n[Reinforcement Learning](https://github.com/sk-g/readings#reinforcement-learning)\r\n\r\n[Attacks on ML Models](https://github.com/sk-g/readings#adversarial-attacks)\r\n\r\n[Privacy in ML](https://github.com/sk-g/readings/blob/master/ReadMe.md#privacy-in-machine-learning)\r\n\r\n\r\n## Convolutional Neural Networks\r\n\r\n[Dynamic Routing Between Capsules](https://papers.nips.cc/paper/6975-dynamic-routing-between-capsules.pdf)\r\n\r\n[How to double dip into your holdout set](https://www.zillow.com/data-science/double-dip-holdout-set/)\r\n\r\n[MMdnn - Microsoft framework for converting models from framework to framework](https://github.com/Microsoft/MMdnn \"Code\")\r\n\r\n[Variational Dropout Sparsifies Deep Neural Networks](https://arxiv.org/abs/1701.05369 \"Paper\") [Code](https://github.com/ars-ashuha/variational-dropout-sparsifies-dnn \"Code\")\r\n\r\n[Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://arxiv.org/pdf/1506.01497)\r\n\r\n[Going Deeper with Convolutions](https://www.cs.unc.edu/~wliu/papers/GoogLeNet.pdf)\r\n\r\n[Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://arxiv.org/abs/1502.03167)\r\n\r\n[Rethinking the Inception Architecture for Computer Vision](https://arxiv.org/abs/1512.00567)\r\n## Interesting Blog Posts and Quora Answers\r\n\r\n[Colah's Visualizing MNIST](http://colah.github.io/posts/2014-10-Visualizing-MNIST/)\r\n\r\n[Great Blog Post about GANS by Nvidia](https://blogs.nvidia.com/blog/2017/05/17/generative-adversarial-network/)\r\n\r\n\r\n## Generative Adversarial Networks\r\n\r\n\r\n[Super Awesome list of GAN papers and their code](https://github.com/zhangqianhui/AdversarialNetsPapers \"Adversarial Nets Papers\")\r\n\r\n[A tutorial on Energy based Learning](https://scholar.google.com/citations?citation_for_view=WLN3QrAAAAAJ%3A8k81kl-MbHgC\u0026cstart=20\u0026hl=en\u0026pagesize=80\u0026user=WLN3QrAAAAAJ\u0026view_op=view_citation \"Yann LeCun\")\r\n\r\n[Generative Visual Manipulation on the Natural Image Manifold](http://people.eecs.berkeley.edu/~junyanz/projects/gvm/)\r\n\r\n[Connecting Generative Adversarial Networks and Actor-Critic Methods](https://arxiv.org/pdf/1610.01945.pdf)\r\n\r\n[Wasserstein GAN](https://arxiv.org/abs/1701.07875 \"Facebook\")\r\n\r\n[Learning from Simulated and Unsupervised Images through Adversarial Training](https://arxiv.org/pdf/1612.07828.pdf \"Apple Inc\")\r\n\r\n[Adversarial Feature Matching for Text Generation](https://arxiv.org/pdf/1706.03850.pdf \"Paper\") [Code](https://github.com/dreasysnail/textGAN_public \"Code\")\r\n\r\n[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\")\r\n## Reinforcement Learning\r\n\r\n[Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning](https://arxiv.org/abs/1712.06567)\r\n\r\n[Evolution Strategies as a Scalable Alternative to Reinforcement Learning](https://arxiv.org/pdf/1703.03864.pdf)\r\n\r\n\r\n## Adversarial Attacks\r\n\r\n[Adversarial Patch](https://arxiv.org/pdf/1712.09665.pdf)\r\n\r\n[CleverHans, benchmarking tool for ML model robustness against adversarial attacks](https://github.com/tensorflow/cleverhans \"Code\")\r\n\r\n## Privacy in Machine Learning\r\n[Deep Learning With Differential Privacy](https://arxiv.org/pdf/1607.00133.pdf)\r\n\r\n[Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data](https://arxiv.org/pdf/1610.05755.pdf)\r\n\r\n[Membership Inference Attacks Against Machine Learning Models](https://arxiv.org/pdf/1610.05820.pdf)\r\n\r\n[The reusable holdout: Preserving validity in adaptive data analysis](https://pdfs.semanticscholar.org/25fe/96591144f4af3d8f8f79c95b37f415e5bb75.pdf)\r\n\r\n[Scalable Private Learning with PATE](https://openreview.net/forum?id=rkZB1XbRZ)\r\n\r\n[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)\r\n\r\n[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)\r\n\r\n[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)\r\n\r\n## NLP/Language Modeling with NNs\r\n\r\n[Fast Text](https://fasttext.cc/) [Introduction](https://research.fb.com/fasttext/ \"Facebook Research\") [Code](https://github.com/facebookresearch/fastText)\r\n\r\n[UNSUPERVISED NEURAL MACHINE TRANSLATION](https://arxiv.org/pdf/1710.11041.pdf \"paper\") [Code](https://github.com/artetxem/undreamt \"Code\")\r\n[SUBWORD LANGUAGE MODELING WITH NEURAL NETWORKS](http://www.fit.vutbr.cz/~imikolov/rnnlm/char.pdf \"paper\")\r\n\r\n[Data Noising as Smoothing in Neural Network Language Models](https://arxiv.org/pdf/1703.02573 \"paper\")\r\n\r\n[Highway Networks](https://arxiv.org/pdf/1505.00387 \"paper\")\r\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsk-g%2Freadings","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsk-g%2Freadings","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsk-g%2Freadings/lists"}