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| 第3行： |
第3行： |
| | * Bootstrapping dialog systems with word embedding[Rong Liu] | | * Bootstrapping dialog systems with word embedding[Rong Liu] |
| | * Neural machine translation by jointly learning to align and translate[Dongxu Zhang] | | * Neural machine translation by jointly learning to align and translate[Dongxu Zhang] |
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| − | Deep Learning and Representation Learning Workshop: NIPS 2014
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| − | Search this site
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| − | HOME
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| − | ACCEPTED PAPERS
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| − | CALL FOR PAPERS
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| − | INVITED SPEAKERS
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| − | PROGRAM COMMITTEE
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| − | SCHEDULE
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| − | SUBMISSION
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| − | SITEMAP
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| − | Accepted papers
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| − | Oral presentations:
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| − | cuDNN: Efficient Primitives for Deep Learning (#49)
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| − | Sharan Chetlur, Cliff Woolley, Philippe Vandermersch, Jonathan Cohen, John Tran, Bryan Catanzaro, Evan Shelhamer
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| − | Distilling the Knowledge in a Neural Network (#65)
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| − | Geoffrey Hinton, Oriol Vinyals, Jeff Dean
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| − | Supervised Learning in Dynamic Bayesian Networks (#54)
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| − | Shamim Nemati, Ryan Adams
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| − | Deeply-Supervised Nets (#2)
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| − | Chen-Yu Lee, Saining Xie, Patrick Gallagher, Zhengyou Zhang, Zhuowen Tu
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| − | Posters, morning session (11:30-14:45):
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| − | Unsupervised Feature Learning from Temporal Data (#3)
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| − | Ross Goroshin, Joan Bruna, Arthur Szlam, Jonathan Tompson, David Eigen, Yann LeCun
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| − | Autoencoder Trees (#5)
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| − | Ozan Irsoy, Ethem Alpaydin
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| − | Scheduled denoising autoencoders (#6)
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| − | Krzysztof Geras, Charles Sutton
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| − | Learning to Deblur (#8)
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| − | Christian Schuler, Michael Hirsch, Stefan Harmeling, Bernhard Schölkopf
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| − | A Winner-Take-All Method for Training Sparse Convolutional Autoencoders (#10)
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| − | Alireza Makhzani, Brendan Frey
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| − | "Mental Rotation" by Optimizing Transforming Distance (#11)
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| − | Weiguang Ding, Graham Taylor
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| − | On Importance of Base Model Covariance for Annealing Gaussian RBMs (#12)
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| − | Taichi Kiwaki, Kazuyuki Aihara
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| − | Ultrasound Standard Plane Localization via Spatio-Temporal Feature Learning with Knowledge Transfer (#14)
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| − | Hao Chen, Dong Ni, Ling Wu, Sheng Li, Pheng Heng
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| − | Understanding Locally Competitive Networks (#15)
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| − | Rupesh Srivastava, Jonathan Masci, Faustino Gomez, Jurgen Schmidhuber
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| − | Unsupervised pre-training speeds up the search for good features: an analysis of a simplified model of neural network learning (#18)
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| − | Avraham Ruderman
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| − | Analyzing Feature Extraction by Contrastive Divergence Learning in RBMs (#19)
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| − | Ryo Karakida, Masato Okada, Shun-ichi Amari
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| − | Deep Tempering (#20)
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| − | Guillaume Desjardins, Heng Luo, Aaron Courville, Yoshua Bengio
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| − | Learning Word Representations with Hierarchical Sparse Coding (#21)
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| − | Dani Yogatama, Manaal Faruqui, Chris Dyer, Noah Smith
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| − | Deep Learning as an Opportunity in Virtual Screening (#23)
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| − | Thomas Unterthiner, Andreas Mayr, Günter Klambauer, Marvin Steijaert, Jörg Wenger, Hugo Ceulemans, Sepp Hochreiter
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| − | Revisit Long Short-Term Memory: An Optimization Perspective (#24)
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| − | Qi Lyu, J Zhu
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| − | Locally Scale-Invariant Convolutional Neural Networks (#26)
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| − | Angjoo Kanazawa, David Jacobs, Abhishek Sharma
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| − | Deep Exponential Families (#28)
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| − | Rajesh Ranganath, Linpeng Tang, Laurent Charlin, David Blei
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| − | Techniques for Learning Binary Stochastic Feedforward Neural Networks (#29)
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| − | Tapani Raiko, mathias Berglund, Guillaume Alain, Laurent Dinh
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| − | Inside-Outside Semantics: A Framework for Neural Models of Semantic Composition (#30)
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| − | Phong Le, Willem Zuidema
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| − | Deep Multi-Instance Transfer Learning (#32)
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| − | Dimitrios Kotzias, Misha Denil, Phil Blunsom, Nando De Freitas
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| − | Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models (#33)
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| − | Ryan Kiros, Ruslan Salakhutdinov, Richard Zemel
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| − | Retrofitting Word Vectors to Semantic Lexicons (#34)
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| − | Manaal Faruqui, Jesse Dodge, Sujay Jauhar, Chris Dyer, Eduard Hovy, Noah Smith
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| − | Deep Sequential Neural Network (#35)
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| − | Ludovic Denoyer, Patrick Gallinari
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| − | Efficient Training Strategies for Deep Neural Network Language Models (#71)
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| − | Holger Schwenk
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| − | Posters, afternoon session (17:00-18:30):
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| − | Deep Learning for Answer Sentence Selection (#36)
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| − | Lei Yu, Karl Moritz Hermann, Phil Blunsom, Stephen Pulman
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| − | Synthetic Data and Artificial Neural Networks for Natural Scene Text Recognition (#37)
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| − | Max Jaderberg, Karen Simonyan, Andrea Vedaldi, Andrew Zisserman
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| − | Learning Torque-Driven Manipulation Primitives with a Multilayer Neural Network (#39)
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| − | Sergey Levine, Pieter Abbeel
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| − | SimNets: A Generalization of Convolutional Networks (#41)
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| − | Nadav Cohen, Amnon Shashua
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| − | Phonetics embedding learning with side information (#44)
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| − | Gabriel Synnaeve, Thomas Schatz, Emmanuel Dupoux
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| − | End-to-end Continuous Speech Recognition using Attention-based Recurrent NN: First Results (#45)
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| − | Jan Chorowski, Dzmitry Bahdanau, KyungHyun Cho, Yoshua Bengio
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| − | BILBOWA: Fast Bilingual Distributed Representations without Word Alignments (#46)
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| − | Stephan Gouws, Yoshua Bengio, Greg Corrado
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| − | Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling (#47)
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| − | Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, Yoshua Bengio
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| − | Reweighted Wake-Sleep (#48)
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| − | Jorg Bornschein, Yoshua Bengio
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| − | Explain Images with Multimodal Recurrent Neural Networks (#51)
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| − | Junhua Mao, Wei Xu, Yi Yang, Jiang Wang, Alan Yuille
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| − | Rectified Factor Networks and Dropout (#53)
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| − | Djork-Arné Clevert, Thomas Unterthiner, Sepp Hochreiter
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| − | Towards Deep Neural Network Architectures Robust to Adversarials (#55)
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| − | Shixiang Gu, Luca Rigazio
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| − | Making Dropout Invariant to Transformations of Activation Functions and Inputs (#56)
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| − | Jimmy Ba, Hui Yuan Xiong, Brendan Frey
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| − | Aspect Specific Sentiment Analysis using Hierarchical Deep Learning (#58)
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| − | Himabindu Lakkaraju, Richard Socher, Chris Manning
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| − | Deep Directed Generative Autoencoders (#59)
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| − | Sherjil Ozair, Yoshua Bengio
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| − | Conditional Generative Adversarial Nets (#60)
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| − | Mehdi Mirza, Simon Osindero
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| − | Analyzing the Dynamics of Gated Auto-encoders (#61)
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| − | Daniel Im, Graham Taylor
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| − | Representation as a Service (#63)
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| − | Ouais Alsharif, Joelle Pineau, philip bachman
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| − | Provable Methods for Training Neural Networks with Sparse Connectivity (#66)
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| − | Hanie Sedghi, Anima Anandkumar
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| − | Trust Region Policy Optimization (#67)
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| − | John D. Schulman, Philipp C. Moritz, Sergey Levine, Michael I. Jordan, Pieter Abbeel
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| − | Document Embedding with Paragraph Vectors (#68)
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| − | Andrew Dai, Christopher Olah, Quoc Le, Greg Corrado
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| − | Backprop-Free Auto-Encoders (#69)
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| − | Dong-Hyun Lee, Yoshua Bengio
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| − | Rate-Distortion Auto-Encoders (#73)
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| − | Luis Sanchez Giraldo, Jose Principe
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