cp:2011

# Most Cited Deep Learning Papers

## 2016

• Unordered List ItemDermatologist-level classification of skin cancer with deep neural networks (2017), A. Esteva et al. [html]
• Weakly supervised object localization with multi-fold multiple instance learning (2017), R. Gokberk et al. [pdf]
• Brain tumor segmentation with deep neural networks (2017), M. Havaei et al. [pdf]
• Professor Forcing: A New Algorithm for Training Recurrent Networks (2016), A. Lamb et al. [pdf]
• Adversarially learned inference (2016), V. Dumoulin et al. [web][pdf]
• Understanding convolutional neural networks (2016), J. Koushik [pdf]
• Taking the human out of the loop: A review of bayesian optimization (2016), B. Shahriari et al. [pdf]
• Adaptive computation time for recurrent neural networks (2016), A. Graves [pdf]
• Densely connected convolutional networks (2016), G. Huang et al. [pdf]
• Continuous deep q-learning with model-based acceleration (2016), S. Gu et al. [pdf]
• A thorough examination of the cnn/daily mail reading comprehension task (2016), D. Chen et al. [pdf]
• Achieving open vocabulary neural machine translation with hybrid word-character models, M. Luong and C. Manning. [pdf]
• Very Deep Convolutional Networks for Natural Language Processing (2016), A. Conneau et al. [pdf]
• Bag of tricks for efficient text classification (2016), A. Joulin et al. [pdf]
• Efficient piecewise training of deep structured models for semantic segmentation (2016), G. Lin et al. [pdf]
• Learning to compose neural networks for question answering (2016), J. Andreas et al. [pdf]
• Perceptual losses for real-time style transfer and super-resolution (2016), J. Johnson et al. [pdf]
• Reading text in the wild with convolutional neural networks (2016), M. Jaderberg et al. [pdf]
• What makes for effective detection proposals? (2016), J. Hosang et al. [pdf]
• Inside-outside net: Detecting objects in context with skip pooling and recurrent neural networks (2016), S. Bell et al. [pdf].
• Instance-aware semantic segmentation via multi-task network cascades (2016), J. Dai et al. [pdf]
• Conditional image generation with pixelcnn decoders (2016), A. van den Oord et al. [pdf]
• Deep networks with stochastic depth (2016), G. Huang et al., [pdf]
• Generative Short Term Stochastic Gibbs Networks 2016), I. Lenz et al. [pdf]

## 2015

• Unordered List ItemSpatial transformer network (2015), M. Jaderberg et al., [pdf]
• Exploring models and data for image question answering (2015), M. Ren et al. [pdf]
• Are you talking to a machine? dataset and methods for multilingual image question (2015), H. Gao et al. [pdf]
• Mind's eye: A recurrent visual representation for image caption generation (2015), X. Chen and C. Zitnick. [pdf]
• From captions to visual concepts and back (2015), H. Fang et al. [pdf].
• Towards AI-complete question answering: A set of prerequisite toy tasks (2015), J. Weston et al. [pdf]
• Ask me anything: Dynamic memory networks for natural language processing (2015), A. Kumar et al. [pdf]
• Unsupervised learning of video representations using LSTMs (2015), N. Srivastava et al. [pdf]
• Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding (2015), S. Han et al. [pdf]
• Improved semantic representations from tree-structured long short-term memory networks (2015), K. Tai et al. [pdf]
• Character-aware neural language models (2015), Y. Kim et al. [pdf]
• Grammar as a foreign language (2015), O. Vinyals et al. [pdf]
• Trust Region Policy Optimization (2015), J. Schulman et al. [pdf]
• Beyond short snippents: Deep networks for video classification (2015) [pdf]
• Learning Deconvolution Network for Semantic Segmentation (2015), H. Noh et al. [pdf]
• Learning spatiotemporal features with 3d convolutional networks (2015), D. Tran et al. [pdf]
• Understanding neural networks through deep visualization (2015), J. Yosinski et al. [pdf]
• An Empirical Exploration of Recurrent Network Architectures (2015), R. Jozefowicz et al. [pdf]
• Training very deep networks (2015), R. Srivastava et al. [pdf]
• Deep generative image models using a￼ laplacian pyramid of adversarial networks (2015), E.Denton et al. [pdf]
• Gated Feedback Recurrent Neural Networks (2015), J. Chung et al. [pdf]
• Fast and accurate deep network learning by exponential linear units (ELUS) (2015), D. Clevert et al. [pdf]
• Pointer networks (2015), O. Vinyals et al. [pdf]
• Visualizing and Understanding Recurrent Networks (2015), A. Karpathy et al. [pdf]
• Attention-based models for speech recognition (2015), J. Chorowski et al. [pdf]
• End-to-end memory networks (2015), S. Sukbaatar et al. [pdf]
• Describing videos by exploiting temporal structure (2015), L. Yao et al. [pdf]
• A neural conversational model (2015), O. Vinyals and Q. Le. [pdf]

## 2014 or earlier

• Unordered List ItemLearning a Deep Convolutional Network for Image Super-Resolution (2014, C. Dong et al. [pdf]
• Recurrent models of visual attention (2014), V. Mnih et al. [pdf]
• Empirical evaluation of gated recurrent neural networks on sequence modeling (2014), J. Chung et al. [pdf]
• Addressing the rare word problem in neural machine translation (2014), M. Luong et al. [pdf]
• On the properties of neural machine translation: Encoder-decoder approaches (2014), K. Cho et. al.
• Recurrent neural network regularization (2014), W. Zaremba et al. [pdf]
• Intriguing properties of neural networks (2014), C. Szegedy et al. [pdf]
• Towards end-to-end speech recognition with recurrent neural networks (2014), A. Graves and N. Jaitly. [pdf]
• Scalable object detection using deep neural networks (2014), D. Erhan et al. [pdf]
• On the importance of initialization and momentum in deep learning (2013), I. Sutskever et al. [pdf]
• Regularization of neural networks using dropconnect (2013), L. Wan et al. [pdf]
• Learning Hierarchical Features for Scene Labeling (2013), C. Farabet et al. [pdf]
• Linguistic Regularities in Continuous Space Word Representations (2013), T. Mikolov et al. [pdf]
• Large scale distributed deep networks (2012), J. Dean et al. [pdf]