Thursday, July 8, 2010

ICML papers on Deep Learning

In the following, we first list some papers published since 2008, to reflect the new research activities since the last deep learning workshop held at NIPS, Dec 2007, and then list some earlier papers as well.

Papers published since 2008

Papers published before 2008


Ahmed et al., 2008
Ahmed, A., Yu, K., Xu, W., Gong, Y., & Xing, E. P. (2008). 
Training hierarchical feed-forward visual recognition models using transfer learning from pseudo tasks. 
European Conference on Computer Visionpdf

Bengio, 2009
Bengio, Y. (2009). 
Learning deep architectures for AI. 
To appear in Foundations and Trends in Machine Learningpdf

Bengio et al., 2007
Bengio, Y., Lamblin, P., Popovici, D., & Larochelle, H. (2007). 
Greedy layer-wise training of deep networks. 
Neural Information Processing Systemspdf

Bengio & LeCun, 2007
Bengio, Y., & LeCun, Y. (2007). 
Scaling learning algorithms towards AI. 
Large Scale Kernel Machinespdf

Collobert & Weston, 2008
Collobert, R., & Weston, J. (2008). 
A unified architecture for natural language processing: Deep neural networks with multitask learning. 
International Conference on Machine Learningpdf

Hinton, 2006
Hinton, G. (2006). 
To recognize shapes, first learn to generate images. 
Technical Reportpdf

Hinton et al., 2006
Hinton, G., Osindero, S., & Teh., Y.-W. (2006). 
A fast learning algorithm for deep belief nets. 
Neural Computation18, 1527-1554. pdf

Hinton & Salakhutdinov, 2006
Hinton, G. E., & Salakhutdinov, R. R. (2006). 
Reducing the dimensionality of data with neural networks. 
Science313, 504 - 507. pdf

Karklin & Lewicki, 2008
Karklin, Y., & Lewicki, M. (2008). 
Emergence of complex cell properties by learning to generalize in natural scenes. 

Larochelle et al., 2007
Larochelle, H., Erhan, D., Courville, A., Bergstra, J., & Bengio, Y. (2007). 
An empirical evaluation of deep architectures on problems with many factors of variation. 

Le Roux & Bengio, 2008
Le Roux, N., & Bengio, Y. (2008). 
Representational power of restricted Boltzmann machines and deep belief networks. 
Neural Computationpdf

Lee et al., 2008
Lee, H., Ekanadham, C., & Ng, A. Y. (2008). 
Sparse deep belief network model for visual area V2. 
Neural Information Processing Systemspdf

Lee et al., 1998
Lee, T., Mumford, D., Romero, R., & Lamme, V. (1998). 
The role of the primary visual cortex in higher level vision. 
Vision research38, 2429-2454. pdf

Mnih & Hinton, 2009
Mnih, A., & Hinton, G. (2009). 
A scalable hierarchical distributed language model. 
Neural Information Processing Systemspdf

Ranzato et al., 2008
Ranzato, M., Boureau, Y.-L., & LeCun, Y. (2008). 
Sparse feature learning for deep belief networks. 
Neural Information Processing Systemspdf

Ranzato et al., 2007
Ranzato, M., Huang, F.-J., Boureau, Y.-L., & LeCun, Y. (2007). 
Unsupervised learning of invariant feature hierarchies with applications to object recognition. 
IEEE Conference on Computer Vision and Pattern Recognitionpdf

Ranzato & Szummer, 2008
Ranzato, M., & Szummer, M. (2008). 
Semi-supervised learning of compact document representations with deep networks. 
International Conferenece on Machine Learningpdf

Salakhutdinov & Hinton, 2007
Salakhutdinov, R., & Hinton, G. (2007). 
Semantic hashing. 
SIGIR workshop on Information Retrieval and applications of Graphical Modelspdf

Salakhutdinov & Hinton, 2008
Salakhutdinov, R., & Hinton, G. (2008). 
Using deep belief nets to learn covariance kernels for Gaussian Processes. 
Neural Information Processing Systemspdf

Salakhutdinov & Murray, 2008
Salakhutdinov, R., & Murray, I. (2008). 
On the quantitative analysis of deep belief networks. 
International Conference on Machine Learningpdf

Torralba et al., 2008
Torralba, A., Fergus, R., & Weiss, Y. (2008). 
Small codes and large image databases for recognition. 
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognitionpdf

Vincent et al., 2008
Vincent, P., Larochelle, H., Bengio, Y., & Manzagol, P.-A. (2008). 
Extracting and composing robust features with denoising autoencoders. 
International Conference on Machine Learningpdf

Welling et al., 2004
Welling, M., Rosen-Zvi, M., & Hinton, G. E. (2004). 
Exponential family harmoniums with an application to information retrieval. 

Weston et al., 2008
Weston, J., Ratle, F., & Collobert, R. (2008). 
Deep learning via semi-supervised embedding. 
International Conference on Machine Learningpdf

Yu et al., 2009
Yu, K., Xu, W., & Gong, Y. (2009). 
Deep learning with kernel regularization for visual recognition. 
Neural Information Processing Systemspdf

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