Text-2015-01-14
- E. Strubell,L. Vilnis,and A.McCallum "Training for fast sequential prediction using dynamic feature selection"[1](Dong Wang)
- "Predictive Property of Hidden Representations in Recurrent Neural Network Language Models."(Xiaoxi Wang)
- "embedding word tokens using a linear dynamical system"[2](Bin Yuan)
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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 评论 Commenting disabled due to a network error. Please reload the page. You do not have permission to add comments. 登录|最近的网站活动|举报滥用行为|打印页面|由 Google 协作平台强力驱动
