Yoshua Bengio  


Yoshua Bengio
Professeur titulaire
Département d'informatique et de recherche opérationnelle
Chaire de Recherche du Canada sur les algorithmes d'apprentissage statistique
<mon prénom> (point) <mon nom de famille> (A commercial) umontreal (point) ca

 

 

Présentations récentes


"Understanding and Improving Deep Learning Algorithms", April 22nd 2010, CMU, Machine Learning Google Distinguished Lecture, Pittsburgh, Pennsylvania, USA. (pdf slides)

"Deep Learning for Speech and Language", December 12th 2009, NIPS 2009 Workshop on Deep Learning for Speech Recognition and Related Applications, Whistler, British Columbia, Canada. (pdf slides)

"Learning Deep Hierarchies of Representations", September 23rd 2009, Google Research, Mountain View, California, USA. (pdf slides) (mp4 video)

"Learning Deep Feature Hierarchies", September 21st 2009, Stanford University, Stanford, California, USA. (pdf)

"Learning Deep Architectures", August 6th, 2009, Toronto CIFAR Summer School 2009, Ontario, Canada. (pdf)

"On The Difficulty of Training Deep Architectures", July 8th 2009, Deep Learning Workshop, Gatsby Unit, UCL, London, U.K. (pdf)

"Learning Deep Architectures", July 7th 2009, Microsoft Research, Cambridge, U.K. (pdf)

"Learning Deep Architectures", UAI'2009, June 19th 2009, Montreal, Qc, Canada. (pdf)

"Tutorial: Learning Deep Architectures", ICML'2009 Workshop on Learning Feature Hierarchies, June 18th 2009, Montreal, Qc, Canada. (pdf)

"Learning Deep Architectures: a Stochastic Optimization Challenge", May 9th, 2009, CIFAR NCAP Workshop, Millcroft Inn, Ontario, Canada. (pdf)

"Learning Deep Architectures: a Stochastic Optimization Challenge", May 6th, 2009, Waterloo University, Waterloo, Ontario, Canada. (pdf)

"Bebe IA: de l'humain a la machine", Cegep de Valleyfield, 28th April, 2009, Valleyfield, Qc, Canada. (pdf)

"Curriculum Learning", Learning Workshop (selected for oral presentation), April 16th 2009, Clearwater, Florida, USA. (pdf)(similar talk given at ICML2009 pdf)

"Statistical Machine Learning for Target Classification from Acoustic Signature", Army Research Lab, January 15th, 2009, Adelphi, Maryland, USA.

"Learning Algorithms for Deep Architectures", NIPS'2008 Workshop on Machine Learning Meets Human Learning, December 12th, 2008, Whistler, B.C., Canada. (pdf)

"On the difficulty of training deep neural nets and the effect of unsupervised pre-training", CIFAR NCAP Workshop, December 7th, 2008, Vancouver, B.C., Canada.

"Machine learning and the curse of highly-variable functions", November 14th, 2008, Montreal Music and Machine Learning Workshop 2008, Montreal, Qc, Canada. (pdf)

"Bebe IA: de l'humain a la machine", University of Montreal, Oct 7th, 2008 Montreal, Canada. (pdf)

"Learning Deep Representations", DARPA Deep Learning Workshop, Sept 25th, 2008, Virginia, USA. (pdf)

"Learning deep architectures for AI", University of Siena, May 29th, 2008, Siena, Italy. (pdf)

"Learning deep architectures for AI", University of Florence, May 26th, 2008 Florence, Italy. (pdf)

"Optimizing Deep Architectures", NIPS Satellite Meeting on Deep Learning December 6th, 2007, Vancouver, BC.

"Deep architectures for Baby AI", CIFAR Summer School August 7-11th, 2007, University of Toronto, Toronto, ON.

"Learning long-term dependencies with recurrent networks", CIFAR NCAP Workshop on sequential data June 14th, 2007, Millcroft Inn, Alton, ON.

"Non-Local and Deep Representations for AI", NIPS 2006 Workshop on Novel Applications of Dimensionality Reduction, December 9th, 2006, Whistler, BC.

"Facing Non-Convex Optimization to Scale Machine Learning to AI", Workshop on Data Mining and Mathematical Programming October 10th, 2006, Montreal, Qc.

"Manifold Learning, Semi-Supervised Learning, and the Curse of Dimensionality", NPCDS/MITACS Spring School on Statistical and Machine Learning: Topics at the Interface May 27th, 2006, Montreal, Qc.

"On the challenge of learning complex functions", Computational Neuroscience Symposium, From Theory to Neurons and Back Again. May 9th, 2006, Montreal, Qc.

"Scaling Learning Algorithms towards AI", NIPS 2005 Large-Scale Kernel Machines Workshop, December 9th, 2005, Whistler, BC.

"Kernel Portfolio Management", NIPS 2005 Machine Learning in Finance Workshop, December 9th, 2005, Whistler, BC.

"Neural Networks, Convexity, Kernels and Curses", August 26th, 2005, Montreal, MITACS Workshop on Statistical Learning of Complex Data from Complex Distributions.

"Reassuring and Troubling Views on Graph-Based Semi-Supervised Learning", August 7th, 2005, Bonn, ICML'2005 Workshop on Partially Supervised Learning.

"Neural Networks, Convexity, Kernels and Curses", August 5th, 2005, Hebrew University, Jerusalem, Israel.

"Curse of Dimensionality: Local versus Non-Local Learning", April 25th, 2005, Montreal, CIAR Manifold Learning Workshop.

"The Curse of Dimensionality for Local Kernel Machines", The Learning Workshop, April 7th, 2005, Snowbird, Utah.

"The Curse of Dimensionality for Local Learning", Microsoft Research, May 25th, 2005, Redmond, Washington.

"Curse of Dimensionality: Local versus Non-Local Learning", CIAR Manifold learning workshop, April 25th, 2005, Montreal.

"Non-Local Learning of Geometric Invariants and Beyond", CIAR Neural Computation Workshop, December 12th, 2004, Vancouver.

"Statistical Learning from High Dimensional and Complex Data: Not a Lost Cause", Fields Institute Data Mining Workshop, October 28th, 2004.

"Local and Non-Local Manifold Learning", University of Toronto, August 2004.

"Local and Non-Local Manifold Learning", MIT, August 2004.

"Learning the Density Structure of High-Dimensional Data", Joint Canada-France Meeting of the Mathematical Sciences, Toulouse 2004.

"Learning the Density Structure of High-Dimensional Data", Computer Discovery Conference, Montreal, June 2004.

"Spectral Dimensionality Reduction via Learning Eigenfunctions", NIPS'2003 post-conference workshops, British-Columbia.

"Learning Internal Representations by Capturing the Principal Factors of Variations that Summarize Similarity between Objects", Canadian Institute for Advanced Research, Vancouver, December 2003.

"Learning eigenfunctions to model high-dimensional data", Statistical Society of Canada, McGill University, April 2003.

"No Unbiased Estimator of Variance for K-Fold Cross-Validation", NIPS'2002 post-conference workshops, British-Columbia.

"Statistical Learning from High-Dimensional Data", MITACS Board Meeting, October 2002.