Lr：/* list paper */

2015-01-28T02:08:37Z

‎list paper

Lr：/* ready to share paper */

2015-01-28T02:03:16Z

‎ready to share paper

2015年1月26日 (一) 07:24 Lr

2015-01-26T07:24:21Z

Lr：以“==ready to share paper== ==choose paper== * '''Document Embedding with Paragraph Vectors'''[http://125.178.23.34/wp-content/uploads/2014/12/Document-Embedding-with-...”为内容创建页面

2015-01-26T07:23:38Z

以“==ready to share paper== ==choose paper== * '''Document Embedding with Paragraph Vectors'''[http://125.178.23.34/wp-content/uploads/2014/12/Document-Embedding-with-...”为内容创建页面

新页面

==ready to share paper==

==choose paper==
* '''Document Embedding with Paragraph Vectors'''[http://125.178.23.34/wp-content/uploads/2014/12/Document-Embedding-with-Paragraph-Vectors.pdf] (#68)Andrew Dai, Christopher Olah, Quoc Le, Greg Corrado ('''Rong Liu''')
* '''Deep Learning for Answer Sentence Selection'''[http://arxiv.org/pdf/1412.1632v1.pdf] (#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''')

=list paper=
==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

@@ 第13行： / 第13行： @@
 =list paper=
 ==Deep Learning and Representation Learning Workshop: NIPS 2014 --Accepted papers==
 *Oral presentations:

@@ 第2行： / 第2行： @@
 * '''Deep Learning for Answer Sentence Selection'''[http://arxiv.org/pdf/1412.1632v1.pdf] (#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''')
 ==choose paper==

←上一版本		2015年1月26日 (一) 07:24的版本
第1行：		第1行：
	==ready to share paper==		==ready to share paper==
		+	* '''Deep Learning for Answer Sentence Selection'''[http://arxiv.org/pdf/1412.1632v1.pdf] (#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''')

	==choose paper==		==choose paper==

Text-2015-01-28 - 版本历史

Lr：/* list paper */

Lr：/* ready to share paper */

2015年1月26日 (一) 07:24 Lr

Lr：以“==ready to share paper== ==choose paper== * '''Document Embedding with Paragraph Vectors'''[http://125.178.23.34/wp-content/uploads/2014/12/Document-Embedding-with-...”为内容创建页面