Very deep convolutional networks for large-scale image recognition (2014), K. Simonyan and A. Zisserman [pdf] ✨
Fully convolutional networks for semantic segmentation (2015), J. Long et al. [pdf] ✨
OverFeat: Integrated recognition, localization and detection using convolutional networks (2014), P. Sermanet et al. (LeCun)[pdf]
Visualizing and understanding convolutional networks (2014), M. Zeiler and R. Fergus [pdf] ✨
Maxout networks (2013), I. Goodfellow et al. (Bengio)[pdf]
ImageNet classification with deep convolutional neural networks (2012), A. Krizhevsky et al. (Hinton)[pdf] ✨
Large scale distributed deep networks (2012), J. Dean et al. [pdf] ✨
Deep sparse rectifier neural networks (2011), X. Glorot et al. (Bengio)[pdf]
Image
Imagenet large scale visual recognition challenge (2015), O. Russakovsky et al. [pdf] ✨
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2015), S. Ren et al. [pdf]
DRAW: A recurrent neural network for image generation (2015), K. Gregor et al. [pdf]
Rich feature hierarchies for accurate object detection and semantic segmentation (2014), R. Girshick et al. [pdf]
Learning and transferring mid-Level image representations using convolutional neural networks (2014), M. Oquab et al. [pdf]
DeepFace: Closing the Gap to Human-Level Performance in Face Verification (2014), Y. Taigman et al. (Facebook)[pdf]
Decaf: A deep convolutional activation feature for generic visual recognition (2013), J. Donahue et al. [pdf]
Learning Hierarchical Features for Scene Labeling (2013), C. Farabet et al. (LeCun)[pdf]
Learning hierarchical invariant spatio-temporal features for action recognition with independent subspace analysis (2011), Q. Le et al. [pdf]
Learning mid-level features for recognition (2010), Y. Boureau (LeCun)[pdf]
Caption
Show, attend and tell: Neural image caption generation with visual attention (2015), K. Xu et al. (Bengio)[pdf] ✨
Show and tell: A neural image caption generator (2015), O. Vinyals et al. [pdf] ✨
Long-term recurrent convolutional networks for visual recognition and description (2015), J. Donahue et al. [pdf] ✨
Deep visual-semantic alignments for generating image descriptions (2015), A. Karpathy and L. Fei-Fei [pdf] ✨
Video HumanActivity
Large-scale video classification with convolutional neural networks (2014), A. Karpathy et al. (FeiFei)[pdf]
A survey on human activity recognition using wearable sensors (2013), O. Lara and M. Labrador [pdf]
3D convolutional neural networks for human action recognition (2013), S. Ji et al. [pdf]
Deeppose: Human pose estimation via deep neural networks (2014), A. Toshev and C. Szegedy [pdf]
Action recognition with improved trajectories (2013), H. Wang and C. Schmid [pdf]
WordEmbedding
Glove: Global vectors for word representation (2014), J. Pennington et al. [pdf] ✨
Sequence to sequence learning with neural networks (2014), I. Sutskever et al. [pdf]
Distributed representations of sentences and documents (2014), Q. Le and T. Mikolov [pdf](Google) ✨
Distributed representations of words and phrases and their compositionality (2013), T. Mikolov et al. (Google)[pdf] ✨
Efficient estimation of word representations in vector space (2013), T. Mikolov et al. (Google)[pdf] ✨
Word representations: a simple and general method for semi-supervised learning (2010), J. Turian (Bengio)[pdf]
MachineTranslation QnA
Towards ai-complete question answering: A set of prerequisite toy tasks (2015), J. Weston et al. [pdf]
Neural machine translation by jointly learning to align and translate (2014), D. Bahdanau et al. (Bengio)[pdf] ✨
Learning phrase representations using RNN encoder-decoder for statistical machine translation (2014), K. Cho et al. (Bengio)[pdf]
A convolutional neural network for modelling sentences (2014), N. kalchbrenner et al. [pdf]
Convolutional neural networks for sentence classification (2014), Y. Kim [pdf]
The stanford coreNLP natural language processing toolkit (2014), C. Manning et al. [pdf]
Recursive deep models for semantic compositionality over a sentiment treebank (2013), R. Socher et al. [pdf] ✨
Natural language processing (almost) from scratch (2011), R. Collobert et al. [pdf]
Recurrent neural network based language model (2010), T. Mikolov et al. [pdf]
Speech Etc.
Speech recognition with deep recurrent neural networks (2013), A. Graves (Hinton)[pdf]
Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups (2012), G. Hinton et al. [pdf] ✨
Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition (2012) G. Dahl et al. [pdf] ✨
RL Robotics
Mastering the game of Go with deep neural networks and tree search, D. Silver et al. (DeepMind) [[pdf]](Mastering the game of Go with deep neural networks and tree search)
Human-level control through deep reinforcement learning (2015), V. Mnih et al. (DeepMind)[pdf] ✨
Deep learning for detecting robotic grasps (2015), I. Lenz et al. [pdf]
Playing atari with deep reinforcement learning (2013), V. Mnih et al. (DeepMind)[pdf])
Unsupervised
Building high-level features using large scale unsupervised learning (2013), Q. Le et al. [pdf] ✨
Contractive auto-encoders: Explicit invariance during feature extraction (2011), S. Rifai et al. (Bengio)[pdf]
An analysis of single-layer networks in unsupervised feature learning (2011), A. Coates et al. [pdf]
Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion (2010), P. Vincent et al. (Bengio)[pdf]
A practical guide to training restricted boltzmann machines (2010), G. Hinton [pdf]
Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion (2010), P. Vincent et al. (Bengio)[pdf]
Hardware Software
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems (2016), M. Abadi et al. (Google)[pdf]
MatConvNet: Convolutional neural networks for matlab (2015), A. Vedaldi and K. Lenc [pdf]
Caffe: Convolutional architecture for fast feature embedding (2014), Y. Jia et al. [pdf] ✨
Theano: new features and speed improvements (2012), F. Bastien et al. (Bengio)[pdf]
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent