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
 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
 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
Understanding / 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
