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cp:2011 [2017/02/27 09:19]
hongxin
cp:2011 [2017/02/27 09:22] (current)
hongxin [2015]
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-Spatial ​transformer network (2015), M. Jaderberg et al., [pdf] +  * 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] +  ​* ​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] +  ​* ​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] +  ​* ​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] +  ​* ​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]. +  ​* ​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] +  ​* ​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] +  ​* ​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] +  ​* ​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] +  ​* ​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] +  ​* ​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] +  ​* ​Character-aware neural language models (2015), Y. Kim et al. [pdf] 
-Grammar as a foreign language (2015), O. Vinyals et al. [pdf] +  ​* ​Grammar as a foreign language (2015), O. Vinyals et al. [pdf] 
-Trust Region Policy Optimization (2015), J. Schulman et al. [pdf] +  ​* ​Trust Region Policy Optimization (2015), J. Schulman et al. [pdf] 
-Beyond short snippents: Deep networks for video classification (2015) [pdf] +  ​* ​Beyond short snippents: Deep networks for video classification (2015) [pdf] 
-Learning Deconvolution Network for Semantic Segmentation (2015), H. Noh et al. [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] +  ​* ​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] +  ​* ​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] +  ​* ​An Empirical Exploration of Recurrent Network Architectures (2015), R. Jozefowicz et al. [pdf] 
-Training very deep networks (2015), R. Srivastava 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] +  ​* ​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] +  ​* ​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] +  ​* ​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] +  ​* ​Pointer networks (2015), O. Vinyals et al. [pdf] 
-Visualizing and Understanding Recurrent Networks (2015), A. Karpathy 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] +  ​* ​Attention-based models for speech recognition (2015), J. Chorowski et al. [pdf] 
-End-to-end memory networks (2015), S. Sukbaatar 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] +  ​* ​Describing videos by exploiting temporal structure (2015), L. Yao et al. [pdf] 
-A neural conversational model (2015), O. Vinyals and Q. Le. [pdf] +  ​* ​A neural conversational model (2015), O. Vinyals and Q. Le. [pdf]
-(~2014)+
  
-Learning ​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] +===== 2014 or earlier ===== 
-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. +  * Unordered List ItemLearning ​a Deep Convolutional Network for Image Super-Resolution (2014, C. Dong et al. [pdf] 
-Recurrent neural network regularization (2014), W. Zaremba et al. [pdf] +  ​* ​Recurrent models of visual attention (2014), V. Mnih et al. [pdf] 
-Intriguing properties of neural networks (2014), C. Szegedy et al. [pdf] +  ​* ​Empirical evaluation of gated recurrent neural networks on sequence modeling (2014), J. Chung et al. [pdf] 
-Towards end-to-end speech recognition with recurrent neural networks (2014), A. Graves and N. Jaitly. [pdf] +  ​* ​Addressing the rare word problem in neural machine translation (2014), M. Luong et al. [pdf] 
-Scalable object detection using deep neural networks (2014), D. Erhan et al. [pdf] +  ​* ​On the properties of neural machine translation:​ Encoder-decoder approaches (2014), K. Cho et. al. 
-On the importance of initialization and momentum in deep learning (2013), I. Sutskever et al. [pdf] +  ​* ​Recurrent neural network regularization (2014), W. Zaremba et al. [pdf] 
-Regularization of neural networks using dropconnect (2013), L. Wan et al. [pdf] +  ​* ​Intriguing properties of neural networks (2014), C. Szegedy et al. [pdf] 
-Learning Hierarchical Features for Scene Labeling (2013), C. Farabet et al. [pdf] +  ​* ​Towards end-to-end speech recognition with recurrent neural networks (2014), A. Graves and N. Jaitly. [pdf] 
-Linguistic Regularities in Continuous Space Word Representations (2013), T. Mikolov et al. [pdf] +  ​* ​Scalable object detection using deep neural networks (2014), D. Erhan et al. [pdf] 
-Large scale distributed deep networks (2012), J. Dean 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]
  
cp/2011.txt · Last modified: 2017/02/27 09:22 by hongxin