Facebook is setting up a deep learning team

According to MIT Tech Review, Facebook is going to get into the deep learning band wagon as well in order to do semantic analysis on the facebook posts using deep [...]

Deep Learning makes MIT Tech Review’s list of top-10 breakthroughs of 2013

Another success news about the deep learning, now deep learning is in MIT Tech Review’s list of top-10 breakthroughs [...]

Kevin Duh’s Deep Learning Summary of NIPS 2012

For the ones who couldn’t attend NIPS 2012, Kevin Duh made an informative summary of NIPS 2012 Deep Learning related events, presentations and papers in his [...]

Deep Networks Advance State of Art in Speech

Deep Learning leads to breakthrough in speech recognition at MSR.

Speech Recognition Leaps Forward – Microsoft Research

Dong Yu, researcher at Microsoft Research Redmond, and Frank Seide, senior researcher and research manager with Microsoft Research Asia, have been spearheading this work, and their teams have collaborated on what has developed into a research breakthrough in the use of [...]

LISA Lab Wins the Final Phase of UTLC Challenge

The Unsupervised and Transfer Learning Challenge is now officially over ! The LISA lab performed very well, winning top honours in Phase 2, which consisted in unsupervised learning of robust features that would transfer to a new distribution with new classes. Full contest results can be found here. Unsurprisingly, our strategy relied heavily on deep learning methods, notably [...]

New Challenge Announced

The Unsupervised and Transfer Learning Challenge is being held until April 15. The goal of the challenge is to “[...] devise preprocessing algorithms to create good data representations. The algorithms can be trained with unlabeled data only during phase 1 (unsupervised learning). Some labels (from other classes than those used for evaluation) will be made available during [...]

New Events Page

We’ve added a new events page, containing links to workshops and meetings that are of interest to the deep learning community. Contact us if we missed any or if you’re organizing an event and want it added to [...]

Deep Learning papers at ICML 2010

From the list of accepted papers, judging by title only:

Learning Fast Approximations of Sparse Coding
Karol Gregor, Yann LeCun

Boosted Backpropagation Learning for Training Deep Modular Networks
Alexander Grubb, Drew Bagnell

Deep learning via Hessian-free optimization
James Martens

3D Convolutional Neural Networks for Human Action Recognition
Shuiwang Ji, Wei Xu, Ming Yang, Kai Yu

Restricted Boltzmann Machines are Hard to Approximately Evaluate or Simulate
Phil [...]

Deep learning papers at AISTATS 2010

Full proceedings

Learning the Structure of Deep Sparse Graphical Models
Ryan Adams, Hanna Wallach, Zoubin Ghahramani

Why Does Unsupervised Pre-training Help Deep Learning?
Dumitru Erhan, Aaron Courville, Yoshua Bengio, Pascal Vincent

Efficient Learning of Deep Boltzmann Machines
Ruslan Salakhutdinov, Hugo Larochelle

Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot, Yoshua Bengio

Factored 3-Way Restricted Boltzmann Machines For Modeling Natural Images
Marc’Aurelio Ranzato, Alex [...]

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