cp:2011
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| + | ====== Most Cited Deep Learning Papers ====== | ||
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| + | https://github.com/terryum/awesome-deep-learning-papers | ||
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| + | ===== 2016 ===== | ||
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| + | * 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] | ||
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| + | ===== 2015 ===== | ||
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| + | * Unordered List ItemSpatial transformer network (2015), M. Jaderberg et al., [pdf] | ||
| + | * Ask your neurons: A neural-based approach to answering questions about images (2015), M. Malinowski 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] | ||
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| + | ===== 2014 or earlier ===== | ||
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| + | * 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] | ||
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