rsantana-isg/awesome-deeplearning-papers

Deep Learning and deep reinforcement learning research papers and books.

Awesome Deep learning papers

A list of recent papers regarding deep learning and deep reinforcement learning. They are sorted by time to see the recent papers first. I will renew the recent papers and add notes to these papers.

Table of Contents

• Papers
• Model Zoo
• Courses
• Books
• Videos
• Software

Papers

• 2017 year
  • deep learning
    • deep learning
    • computer vision
    • attention and memory
    • generative learning
    • transfer learning
  • deep reinforcement learning
  • natural language process
    • deep learning
    • attention and memory
• 2016 year
  • deep learning
    • deep learning
    • computer vision
    • attention and memory
    • generative learning
    • transfer learning
    • one/zero shot learning
  • deep reinforcement learning
  • natural language process
    • generative learning
    • attention and memory
• 2015 year
  • deep learning
  • computer vision
  • attention and memory
  • generative learning
  • transfer learning
  • one/zero shot learning
  • deep reinforcement learning
  • natural language process
• 2014 year
  • deep learning
  • generative learning
  • attention and memory
  • deep reinforcement Learning
  • transfer learning
  • natural language process
• 2013 year
  • deep learning
  • transfer learning
  • deep reinforcement Learning
• 2012 year
  • transfer learning
• 2011 year
  • transfer learning
• 2010 year
  • transfer learning
• before 2010 year
  • transfer learning

Model Zoo

| year | name | paper | code | | ------ | ------ | -------- | ------ | ------ | | 2012 | AlexNet | pdf | https://code.google.com/p/cuda-convnet/ | | 2013 | RCNN | arxiv | https://github.com/rbgirshick/rcnn | | 2014 | CGNA | arxiv | https://github.com/zhangqianhui/Conditional-Gans | | 2014 | DeepFaceVariant | pdf | https://github.com/joyhuang9473/deepid-implementation | | 2014 | GAN | arxiv | https://github.com/goodfeli/adversarial | | 2014 | GoogLeNet | pdf | https://github.com/google/inception | | 2014 | OverFeat | arxiv | https://github.com/sermanet/OverFeat | | 2014 | SPPNet | arxiv | https://github.com/yhenon/keras-spp | | 2014 | VAE | arxiv | https://github.com/dpkingma/nips14-ssl | | 2014 | VGGNet | arxiv | https://gist.github.com/ksimonyan/211839e770f7b538e2d8 | | 2015 | DCGAN | arxiv | https://github.com/carpedm20/DCGAN-tensorflow | | 2015 | DRAW | arxiv | https://github.com/ericjang/draw | | 2015 | Global And Local Attention | arxiv | https://github.com/giancds/tsf_nmt | | 2015 | FaceNet | arxiv | https://github.com/davidsandberg/facenet | | 2015 | Fast RCNN | arxiv | https://github.com/rbgirshick/fast-rcnn | | 2015 | Faster RCNN | arxiv | https://github.com/rbgirshick/py-faster-rcnn | | 2015 | FCNT | pdf | https://github.com/scott89/FCNT | | 2015 | Inception | arxiv | https://github.com/tensorflow/models/tree/master/inception | | 2015 | LAPGAN | arxiv | https://github.com/facebook/eyescream | | 2015 | NeuralGPU | arxiv | https://github.com/tensorflow/models/tree/master/neural_gpu | | 2015 | Pointer Net | arxiv | https://github.com/devsisters/pointer-network-tensorflow | | 2015 | ResNet | arxiv1 , arxiv2, arxiv3 | https://github.com/tensorflow/models/tree/master/resnet | | 2015 | Transformer | arxiv | https://github.com/tensorflow/models/tree/master/transformer | | 2016 | Dp_sgd | arxiv | https://github.com/tensorflow/models/tree/master/differential_privacy | | 2016 | EnergyGAN | arxiv | https://github.com/buriburisuri/ebgan | | 2016 | Grad-CAM | arxiv | https://github.com/Ankush96/grad-cam.tensorflow | | 2016 | Im2txt | arxiv | https://github.com/tensorflow/models/tree/master/im2txt | | 2016 | InfoGAN | arxiv | https://github.com/buriburisuri/supervised_infogan | | 2016 | Multiple_teachers | arxiv | https://github.com/tensorflow/models/tree/master/differential_privacy | | 2016 | Neural Programmer | pdf | https://github.com/tensorflow/models/tree/master/neural_programmer | | 2016 | PCNN | arxiv | https://github.com/kundan2510/pixelCNN | | 2016 | PVANet | arxiv | https://github.com/sanghoon/pva-faster-rcnn | | 2016 | R-FCN | arxiv | https://github.com/Orpine/py-R-FCN | | 2016 | SeqGAN | pdf | https://github.com/LantaoYu/SeqGAN | | 2016 | SqueezeNet | arxiv | https://github.com/songhan/SqueezeNet-Deep-Compression | | 2016 | Swivel | arxiv | https://github.com/tensorflow/models/tree/master/swivel | | 2016 | SyntaxNet | arxiv | https://github.com/tensorflow/models/tree/master/syntaxnet | | 2016 | Textsum | | https://github.com/tensorflow/models/tree/master/textsum | | 2016 | VGNA | arxiv | https://github.com/Shuangfei/vgan | | 2017 | SalGAN | arxiv | https://github.com/imatge-upc/saliency-salgan-2017 | | 2017 | WGAN | arxiv | https://github.com/Zardinality/WGAN-tensorflow |

Courses

• [Stanford] CS231n: Convolutional Neural Networks for Visual Recognition
• [CUHK] ELEG 5040: Advanced Topics in Signal Processing(Introduction to Deep Learning)
• [Stanford] CS224d: Deep Learning for Natural Language Processing
• [Oxford] Deep Learning by Prof. Nando de Freitas
• [NYU] Deep Learning by Prof. Yann LeCun
• [Berkeley] CS294: Deep Reinforcement Learning
• [Berkeley] Stat212b：Topics Course on Deep Learning
• [MIT] S094: Deep Learning for Self-Driving Cars
• ELEG 5040: Advanced Topics in Signal Processing (Introduction to Deep Learning)
• [Stanford] CS20SI: Tensorflow for Deep Learning Research
• [Stanford] CS224n: Natural Language Processing with Deep Learning
• [MIT] S191: Introduction to Deep Learning

Books

• Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville, [zh]
• Neural Networks and Deep Learning by Michael Nielsen
• Deep Learning Tutorial by LISA lab, University of Montreal
• 神经网络与深度学习.邱锡鹏
• UFLDL Tutorial
• Rules of Machine Learning: Best Practices for ML Engineering
• Reinforcement Learning: An Introduction [code]
• Reinforcement LearningState-of-the-Art
• A Course in Machine Learning
• 深度学习入门 by PaddlePaddle
• TensorFlow For Machine Intelligence

Videos

• Talks
  • Deep Learning, Self-Taught Learning and Unsupervised Feature Learning By Andrew Ng
  • Recent Developments in Deep Learning By Geoff Hinton
  • The Unreasonable Effectiveness of Deep Learning by Yann LeCun
  • Deep Learning of Representations by Yoshua bengio
• Courses
  • Deep Learning Course – Nando de Freitas@Oxford
  • 吴立德 《深度学习课程》
  • RL Course by David Silver
  • Practical Deep Learning For Coders
  • Udacity Deep Learning Online Course, by google
  • Foundations of Machine Learning Boot Camp

Software

• [C++] Singa: Singa is an Apache Incubating project for developing an open source deep learning library. [Web]
• [C++] Caffe: Deep learning framework by the BVLC [Web] ⭐
• [Python] Chainer bridge the gap between algorithms and implementations of deep learning. [Web]
• [C++] CNTK:The Microsoft Cognitive Toolkit. [Web]
• [Python] DeepPy is a Pythonic deep learning framework built on top of NumPy.[Web]
• [Python] Deepnet: a GPU-based python implementation of deep learning algorithms. [Web]
• [Python] Deepgaze: A computer vision library for human-computer interaction based on CNNs [Web]
• [Java] Deeplearning4J: Neural Net Platform. [Web]
• [Python] Edward: A library for probabilistic modeling, inference, and criticism. [Web]
• [Python] Gensim: Deep learning toolkit implemented in python programming language intended for handling large text collections, using efficient algorithms.[Web]
• [Python] Hebel: A library for deep learning with neural networks in Python using GPU acceleration with CUDA through PyCUDA. [Web]
• [Python] Keras: Deep Learning library for Theano and TensorFlow. [Web] ⭐
• [Julia] Knet: Knet (pronounced "kay-net") is the Koç University deep learning framework implemented in Julia. [Web]
• [Python] Kur: Descriptive Deep Learning. [Web] ⭐
• [Matlab] MatConvNet: CNNs for MATLAB [Web]
• [Julia] Mocha is a Deep Learning framework for Julia, inspired by the C++ framework Caffe. [Web]
• [C++] MXNet: A flexible and efficient deep learning library for heterogeneous distributed systems with multi-language support [Web] ⭐
  • [Python] MinPy: Providing a high performing and flexible deep learning platform, by prototyping a pure NumPy interface above MXNet backend. [Web]
• [Python] Neon is Nervana's Python based Deep Learning framework.[Web]
• [C++] NVIDIA DIGITS is a new system for developing, training and visualizing deep neural networks. [Web]
• [C++] PaddlePaddle (PArallel Distributed Deep LEarning) is an easy-to-use, efficient, flexible and scalable deep learning platform. [Web]
• [C++] Tensorflow: An open source software library for numerical computation using data flow graph by Google [Web] ⭐
• [Python] Theano: Mathematical library in Python, maintained by LISA lab [Web]
  • [Python] Theano-based deep learning libraries: [Pylearn2],
• [Lua] Torch7: Deep learning library in Lua, used by Facebook and Google Deepmind [Web] ⭐
  • Torch-based deep learning libraries: [torchnet], [Blocks], [Lasagne]