Paper List : Paper List.docx
Convolutional Neural Network (12 papers)
 Rethinking the inception architecture for computer vision (2016), C. Szegedy et al.pdf
 Inceptionv4, inceptionresnet and the impact of residual connections on learning (2016), C. Szegedy et al.pdf
 Identity Mappings in Deep Residual Networks (2016), K. He et al.pdf
 Deep residual learning for image recognition (2016), K. He et al.pdf
 Spatial transformer network (2015), M. Jaderberg et al.pdf
 GoingDeeperwithConvolutions  GoogleNet.pdf
 VERY DEEP CONVOLUTIONAL NETWORKS FOR LARGESCALE IMAGE RECOGNITION  VGGNet.pdf
 Return of the devil in the details, delving deep into convolutional nets (2014), K. Chatfield et al.pdf
 OverFeat Integrated recognition, localization and detection using convolutional networks (2013), P. Sermanet et al.pdf
 Maxout networks (2013), I. Goodfellow et al.pdf
 NetworkInNetwork.pdf
 ImageNet classification with deep convolutional neural networks (2012), A. Krizhevsky et al.pdf
Natural Language Processing / Recurrent Neural Network (12 papers)
 Conditional random fields as recurrent neural networks (2015), S. Zheng and S. Jayasumana.pdf
 Effective approaches to attentionbased neural machine translation (2015), M. Luong et al.pdf
 Exploring the limits of language modeling (2016), R. Jozefowicz et al.pdf
 Generating sequences with recurrent neural networks (2013), A. Graves.pdf
 Learning phrase representations using RNN encoderdecoder for statistical machine translation (2014), K. Cho et al..pdf
 Memory networks (2014), J. Weston et al.pdf
 Neural Architectures for Named Entity Recognition (2016), G. Lample et al.pdf
 Neural machine translation by jointly learning to align and translate (2014), D. Bahdanau et al.pdf
 Neural turing machines (2014), A. Graves et al.pdf
 Sequence to sequence learning with neural networks (2014), I. Sutskever et al.pdf
 Teaching machines to read and comprehend (2015), K. Hermann et al.pdf
 Training and analysing deep recurrentneuralnetworks.pdf
Optimization / Training Techniques (10 papers)
 ADAM A METHOD FOR STOCHASTIC OPTIMIZATION.pdf
 Batch normalization Accelerating deep network training by reducing internal covariate shift.pdf
 Delving deep into rectifiers, Surpassing humanlevel performance on imagenet classification (2015), K. He et al.pdf
 Dropout A Simple Way to Prevent Neural Networks from Overﬁtting.pdf
 Improving neural networks by preventing coadaptation of feature detectors (2012), G. Hinton et al.pdf
 Learning longterm dependencies with gradient descent is difﬁcult.pdf
 Learning representations by backpropagating errors.pdf
 Random search for hyperparameter optimization (2012) J. Bergstra and Y. Bengio.pdf
 Training very deep networks (2015), R. Srivastava et al.pdf
 ADADELTA AN ADAPTIVE LEARNING RATE METHOD.pdf
Uderstanding / Generalization / Transfer (7 papers)
 Distilling the knowledge in a neural network (2015), G. Hinton et al..pdf
 Deep neural networks are easily fooled, High confidence predictions for unrecognizable images (2015), A. Nguyen et al.pdf
 How transferable are features in deep neural networks.pdf
 CNN features offtheShelf, An astounding baseline for recognition (2014), A. Razavian et al.pdf
 Learning and transferring midLevel image representations using convolutional neural networks (2014), M. Oquab et al.pdf
 Visualizing and understanding convolutional networks (2014), M. Zeiler and R. Fergus.pdf
 Decaf, A deep convolutional activation feature for generic visual recognition (2014), J. Donahue et al.pdf
Other papers
Recent papers (12 papers)
 Batch renormalization Towards reducing minibatch dependence in batchnormalized models (2017), S. Ioffe.pdf
 Convolutional Sequence to Sequence Learning (2017), Jonas Gehring et al.pdf
 Deep Photo Style Transfer (2017), F. Luan et al.pdf
 Deep voice Realtime neural texttospeech (2017), S. Arik et al.pdf
 Deformable Convolutional Networks (2017), J. Dai et al.pdf
 Evolution Strategies as a Scalable Alternative to Reinforcement Learning (2017), T. Salimans et al.pdf
 Learning to discover crossdomain relations with generative adversarial networks (2017), T. Kim et al.pdf
 Least squares generative adversarial networks (2016), X. Mao et al.pdf
 PixelNet Representation of the pixels, by the pixels, and for the pixels (2017), A. Bansal et al.pdf
 TACOTRON Towards endtoend speech synthesis (2017), Y. Wang et al.pdf
 Understanding deep learning requires rethinking generalization (2017), C. Zhang et al.pdf
 Wasserstein GAN (2017), M. Arjovsky et al.pdf
Classic papers (10 papers)
 A fast learning algorithm for deep belief nets (2006), G. Hinton et al.pdf
 A practical guide to training restricted boltzmann machines (2010), G. Hinton.pdf
 An analysis of singlelayer networks in unsupervised feature learning (2011), A. Coates et al.pdf
 Deep sparse rectifier neural networks (2011), X. Glorot et al.pdf
 Gradientbased learning applied to document recognition (1998), Y. LeCun et al.pdf
 Greedy layerwise training of deep networks (2007), Y. Bengio et al.pdf
 Learning midlevel features for recognition (2010), Y. Boureau.pdf
 Natural language processing (almost) from scratch (2011), R. Collobert et al.pdf
 Understanding the difficulty of training deep feedforward neural networks (2010), X. Glorot and Y. Bengio.pdf
 Why does unsupervised pretraining help deep learning (2010), D. Erhan et al.pdf
