Text-2015-01-21
目录
- Document Embedding with Paragraph Vectors[1] (#68)Andrew Dai, Christopher Olah, Quoc Le, Greg Corrado (Rong Liu)
- Autoencoder Trees (#5)Ozan Irsoy, Ethem Alpaydin(Xi Ma)
- Understanding Locally Competitive Networks (#15)Rupesh Srivastava, Jonathan Masci, Faustino Gomez, Jurgen Schmidhuber (Shallsee)
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- Document Embedding with Paragraph Vectors[2] (#68)Andrew Dai, Christopher Olah, Quoc Le, Greg Corrado (Rong Liu)
- Deep Learning for Answer Sentence Selection[3] (#36)Lei Yu, Karl Moritz Hermann, Phil Blunsom, Stephen Pulman(Tianyi Luo)
- Retrofitting Word Vectors to Semantic Lexicons (#34)Manaal Faruqui, Jesse Dodge, Sujay Jauhar, Chris Dyer, Eduard Hovy, Noah Smith(Chaos)
- Autoencoder Trees (#5)Ozan Irsoy, Ethem Alpaydin(Xi Ma)
- A Winner-Take-All Method for Training Sparse Convolutional Autoencoders (#10)Alireza Makhzani, Brendan Frey (Shallsee)
- Understanding Locally Competitive Networks (#15)Rupesh Srivastava, Jonathan Masci, Faustino Gomez, Jurgen Schmidhuber (Shallsee)
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Deep Learning and Representation Learning Workshop: NIPS 2014 --Accepted papers
- Oral presentations:
cuDNN: Efficient Primitives for Deep Learning (#49)Sharan Chetlur, Cliff Woolley, Philippe Vandermersch, Jonathan Cohen, John Tran, Bryan Catanzaro, Evan Shelhamer
Distilling the Knowledge in a Neural Network (#65)Geoffrey Hinton, Oriol Vinyals, Jeff Dean
Supervised Learning in Dynamic Bayesian Networks (#54)Shamim Nemati, Ryan Adams
Deeply-Supervised Nets (#2)Chen-Yu Lee, Saining Xie, Patrick Gallagher, Zhengyou Zhang, Zhuowen Tu
- Posters, morning session (11:30-14:45):
Unsupervised Feature Learning from Temporal Data (#3)Ross Goroshin, Joan Bruna, Arthur Szlam, Jonathan Tompson, David Eigen, Yann LeCun
Autoencoder Trees (#5)Ozan Irsoy, Ethem Alpaydin
Scheduled denoising autoencoders (#6)Krzysztof Geras, Charles Sutton
Learning to Deblur (#8)Christian Schuler, Michael Hirsch, Stefan Harmeling, Bernhard Schölkopf
A Winner-Take-All Method for Training Sparse Convolutional Autoencoders (#10)Alireza Makhzani, Brendan Frey
"Mental Rotation" by Optimizing Transforming Distance (#11)Weiguang Ding, Graham Taylor
On Importance of Base Model Covariance for Annealing Gaussian RBMs (#12)Taichi Kiwaki, Kazuyuki Aihara
Ultrasound Standard Plane Localization via Spatio-Temporal Feature Learning with Knowledge Transfer (#14)Hao Chen, Dong Ni, Ling Wu, Sheng Li, Pheng Heng
Understanding Locally Competitive Networks (#15)Rupesh Srivastava, Jonathan Masci, Faustino Gomez, Jurgen Schmidhuber
Unsupervised pre-training speeds up the search for good features: an analysis of a simplified model of neural network learning (#18)Avraham Ruderman
Analyzing Feature Extraction by Contrastive Divergence Learning in RBMs (#19)Ryo Karakida, Masato Okada, Shun-ichi Amari
Deep Tempering (#20)Guillaume Desjardins, Heng Luo, Aaron Courville, Yoshua Bengio
Learning Word Representations with Hierarchical Sparse Coding (#21)Dani Yogatama, Manaal Faruqui, Chris Dyer, Noah Smith
Deep Learning as an Opportunity in Virtual Screening (#23)Thomas Unterthiner, Andreas Mayr, Günter Klambauer, Marvin Steijaert, Jörg Wenger, Hugo Ceulemans, Sepp Hochreiter
Revisit Long Short-Term Memory: An Optimization Perspective (#24)Qi Lyu, J Zhu
Locally Scale-Invariant Convolutional Neural Networks (#26)Angjoo Kanazawa, David Jacobs, Abhishek Sharma
Deep Exponential Families (#28)Rajesh Ranganath, Linpeng Tang, Laurent Charlin, David Blei
Techniques for Learning Binary Stochastic Feedforward Neural Networks (#29)Tapani Raiko, mathias Berglund, Guillaume Alain, Laurent Dinh
Inside-Outside Semantics: A Framework for Neural Models of Semantic Composition (#30)Phong Le, Willem Zuidema
Deep Multi-Instance Transfer Learning (#32)Dimitrios Kotzias, Misha Denil, Phil Blunsom, Nando De Freitas
Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models (#33)Ryan Kiros, Ruslan Salakhutdinov, Richard Zemel
Retrofitting Word Vectors to Semantic Lexicons (#34)Manaal Faruqui, Jesse Dodge, Sujay Jauhar, Chris Dyer, Eduard Hovy, Noah Smith
Deep Sequential Neural Network (#35)Ludovic Denoyer, Patrick Gallinari
Efficient Training Strategies for Deep Neural Network Language Models (#71)Holger Schwenk
- Posters, afternoon session (17:00-18:30):
Deep Learning for Answer Sentence Selection (#36)Lei Yu, Karl Moritz Hermann, Phil Blunsom, Stephen Pulman
Synthetic Data and Artificial Neural Networks for Natural Scene Text Recognition (#37)Max Jaderberg, Karen Simonyan, Andrea Vedaldi, Andrew Zisserman
Learning Torque-Driven Manipulation Primitives with a Multilayer Neural Network (#39)Sergey Levine, Pieter Abbeel
SimNets: A Generalization of Convolutional Networks (#41)Nadav Cohen, Amnon Shashua
Phonetics embedding learning with side information (#44)Gabriel Synnaeve, Thomas Schatz, Emmanuel Dupoux
End-to-end Continuous Speech Recognition using Attention-based Recurrent NN: First Results (#45)Jan Chorowski, Dzmitry Bahdanau, KyungHyun Cho, Yoshua Bengio
BILBOWA: Fast Bilingual Distributed Representations without Word Alignments (#46)Stephan Gouws, Yoshua Bengio, Greg Corrado
Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling (#47)Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, Yoshua Bengio
Reweighted Wake-Sleep (#48)Jorg Bornschein, Yoshua Bengio
Explain Images with Multimodal Recurrent Neural Networks (#51)Junhua Mao, Wei Xu, Yi Yang, Jiang Wang, Alan Yuille
Rectified Factor Networks and Dropout (#53)Djork-Arné Clevert, Thomas Unterthiner, Sepp Hochreiter
Towards Deep Neural Network Architectures Robust to Adversarials (#55)Shixiang Gu, Luca Rigazio
Making Dropout Invariant to Transformations of Activation Functions and Inputs (#56)Jimmy Ba, Hui Yuan Xiong, Brendan Frey
Aspect Specific Sentiment Analysis using Hierarchical Deep Learning (#58)Himabindu Lakkaraju, Richard Socher, Chris Manning
Deep Directed Generative Autoencoders (#59)Sherjil Ozair, Yoshua Bengio
Conditional Generative Adversarial Nets (#60)Mehdi Mirza, Simon Osindero
Analyzing the Dynamics of Gated Auto-encoders (#61)Daniel Im, Graham Taylor
Representation as a Service (#63)Ouais Alsharif, Joelle Pineau, philip bachman
Provable Methods for Training Neural Networks with Sparse Connectivity (#66)Hanie Sedghi, Anima Anandkumar
Trust Region Policy Optimization (#67)John D. Schulman, Philipp C. Moritz, Sergey Levine, Michael I. Jordan, Pieter Abbeel
Document Embedding with Paragraph Vectors (#68)Andrew Dai, Christopher Olah, Quoc Le, Greg Corrado
Backprop-Free Auto-Encoders (#69)Dong-Hyun Lee, Yoshua Bengio
Rate-Distortion Auto-Encoders (#73)Luis Sanchez Giraldo, Jose Principe
