Kai Sheng Tai, Richard Socher, Christopher D. Manning, Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks, ACL 2015 [Paper]
(#) Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra, DRAW: A Recurrent Neural Network For Image Generation, arXiv:1502.04623 [Paper]
Dario Amodei, Rishita Anubhai, Eric Battenberg, Carl Case, Jared Casper, Bryan Catanzaro, Jingdong Chen, Mike Chrzanowski, Adam Coates, Greg Diamos, Erich Elsen, Jesse Engel, Linxi Fan, Christopher Fougner, Tony Han, Awni Hannun, Billy Jun, Patrick LeGresley, Libby Lin, Sharan Narang, Andrew Ng, Sherjil Ozair, Ryan Prenger, Jonathan Raiman, Sanjeev Satheesh, David Seetapun, Shubho Sengupta, Yi Wang, Zhiqian Wang, Chong Wang, Bo Xiao, Dani Yogatama, Jun Zhan, Zhenyao Zhu, Deep Speech 2: End-to-End Speech Recognition in English and Mandarin, arXiv:1512.02595, [Paper]
Fabian Caba Heilbron, Victor Escorcia, Bernard Ghanem and Juan Carlos Niebles, Activitynet: A large-scale video benchmark for human activity understanding, CVPR 2015, [Paper]
(#) Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich, Going Deeper with Convolutions, CVPR 2015 [Paper]
(#) Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, Zbigniew Wojna, Rethinking the Inception Architecture for Computer Vision, arXiv:1512.00567, [Paper]
(#) Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning, arXiv:1602.07261, [Paper]
(#) Pierre Sermanet, David Eigen, Xiang Zhang, Michael Mathieu, Rob Fergus, Yann LeCun, OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks, arXiv:1312.6229 [Paper]
Dmitry Ulyanov, Vadim Lebedev, Andrea Vedaldi, Victor Lempitsky Texture Networks: Feed-forward Synthesis of Textures and Stylized Images, arXiv:1603.03417 [Paper]
Qi, Charles R and Su, Hao and Niessner, Matthias and Dai, Angela and Yan, Mengyuan and Guibas, Leonidas J, Volumetric and Multi-View CNNs for Object Classification on 3D Data, arXiv preprint arXiv:1604.03265, 2016, [Paper], [code]
Edgar Simo-Serra, Hiroshi Ishikawa, Fashion Style in 128 Floats: Joint Ranking and Classification using Weak Data for Feature Extraction, CVPR 2016, [Paper]
Adam Paszke, Abhishek Chaurasia, Sangpil Kim, Eugenio Culurciello, ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation, arXiv:1606.02147, [Paper]
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin Riedmiller, Andreas K. Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg, Demis Hassabis, Human-Level Control through Deep Reinforcement Learning, Nature, [Paper]
(#) Marc G. Bellemare, Georg Ostrovski, Arthur Guez, Philip S. Thomas, Rémi Munos, Increasing the Action Gap: New Operators for Reinforcement Learning, arXiv:1512.04860, [Paper]
Jie Fu, Zichuan Lin, Miao Liu, Nicholas Leonard, Jiashi Feng, Tat-Seng Chua, Deep Q-Networks for Accelerating the Training of Deep Neural Networks, arXiv:1606.01467, [Paper]
Sainbayar Sukhbaatar, Arthur Szlam, Gabriel Synnaeve, Soumith Chintala, Rob Fergus, MazeBase: A Sandbox for Learning from Games, arXiv:1511.07401, [Paper]
This project contains code to train a model that automatically plays the first level of Super Mario World using only raw pixels as the input (no hand-engineered features).The used technique is deep Q-learning, as described in the Atari paper (Summary), combined with a Spatial Transformer.
ViZDoom allows developing AI bots that play Doom using only the visual information (the screen buffer). It is primarily intended for research in machine visual learning, and deep reinforcement learning, in particular.
Emily Denton, Soumith Chintala, Arthur Szlam, Rob Fergus, Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks, arXiv:1506.05751 [Paper]
Ke Tran, Arianna Bisazza, Christof Monz, Word Translation Prediction for Morphologically Rich Languages with Bilingual Neural Networks, EMNLP 2014 [Paper]
(#) Po-Sen Huang, Xiaodong He, Jianfeng Gao, Li Deng, Alex Acero, Larry Heck, Learning Deep Structured Semantic Models for Web Search using Clickthrough Data, CIKM 2013 [Paper]
Satoshi Iizuka, Edgar Simo-Serra, Hiroshi Ishikawa, Let there be Color!: Joint End-to-end Learning of Global and Local Image Priors for Automatic Image Colorization with Simultaneous Classification, SIGGRAPH 2016, [Paper]
(#) Akira Fukui, Dong Huk Park, Daylen Yang, Anna Rohrbach, Trevor Darrell, Marcus Rohrbach, Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding, [Paper]
Roozbeh Mottaghi, Hessam Bagherinezhad, Mohammad Rastegari, Ali Farhadi, Newtonian Image Understanding: Unfolding the Dynamics of Objects in Static Images, CVPR 2016, [Paper]
Libraries
Model related
nn : an easy and modular way to build and train simple or complex neural networks [Code][Documentation]
dpnn : extensions to the nn lib, more modules [Code]
nnx : extension to the nn lib, experimental neural network modules and criterions [Code]
torchnet: framework for torch which provides a set of abstractions aiming at encouraging code re-use as well as encouraging modular programming [Code][Paper]
GPU related
distro-cl: An OpenCL distribution for Torch [Code]
torch-models : Implementation of state-of-art models in Torch. [Code]
lutorpy : Lutorpy is a libray built for deep learning with torch in python. [Code]
CoreNLP.lua : Lua client for Stanford CoreNLP. [Code]
Torchlib: Data structures and libraries for Torch. [Code]
THFFmpeg: Torch bindings for FFmpeg (reading videos only) [Code]
tunnel: Data Driven Framework for Distributed Computing in Torch 7, [Code]
pytorch: Python wrappers for torch and lua, [Code]
lutorpy: Use torch in python for deep learning., [Code]
torch-pcl: Point Cloud Library (PCL) bindings for Torch, [Code]
Moses: A Lua utility-belt library for functional programming. It complements the built-in Lua table library, making easier operations on arrays, lists, collections. [Cpde]