# Neural Computation & Adaptive Perception Summer School

## August 7-11, 2007

Toronto, Canada

This summer school is organized as part of the
Neural Computation & Adaptive Perception research program of CIFAR.

It is hosted by the Machine Learning group at the Department of Computer Science, University of Toronto.

## Tutorials

Geoffrey Hinton, "An efficient way to learn deep generative models" [Slides (updated)], "Hierarchies of RBMs" [Slides]

Yoshua Bengio, "Deep Architectures for Baby AI" [Slides]

Bruno A. Olshausen, "Things we know, and don't know, about biological vision" [Slides (Part 1), Slides (Part 2)]

Hugh R. Wilson, "Binocular Rivalry: Waves, Hysteresis and Perceptual Memory" [Slides], "Nonlinear Dynamics and Vision" [Slides]

Yair Weiss, Lightness Perception and Lightness Illusions, Moving Rhombus Displays.

## Talks

- Ruslan Salakhutdinov, "Semantic Hashing" [Slides]

- Pascal Lamblin, "Training Convolution RBMs" [Slides]

- Andriy Mnih, "Three New Models for Statistical Language Modelling" [Slides]

- Raia Hadsell, "Online Learning for Offroad Robots: Using Spatial Label Propagation to Learn Long-Range Traversability" [Slides]

- James Bergstra, "Learning Long Sequences with TRBMs" [Slides]

- Ilya Sutskever, "Modelling Sequences using Temporal Restricted Boltzmann Machines" [Slides]

- Mohammad Emtiyaz Khan, "Multi-Scale Structure Learning" [Slides]

- Matthew Grimes, "Energy-Based Visual SLAM for Outdoor Robotics" [Slides, Movie]

- Sumit Chopra, "Discovering Hidden Structure of House Prices with Relational Latent Manifold Model" [Slides]

- Kristen Fortney, "Learning without synaptic change: a mechanism for sensorimotor control" [Slides]

- Dumitru Erhan, "An Empirical Evaluation of Deep Architectures on Problems with Many Factors of Variation" [Slides]

- Jerome Louradour, "Learning the human world with Deep Belief Networks" [Slides]

- Siwei Lyu, "Modeling Images with Field of Gaussian Scale Mixtures" [Slides]

- Danny Tarlow, "Learning Articulated Skeletons From Motion" [Slides]

- Jascha Sohl-Dickstein, "Cooperative Energy Hierarchy" [Slides]

- Jimmy Wang, "Learning Overcomplete Subspace Structures on Natural Speech Signal" [Slides]

- Nicolas Heess, "Adaptive Coding of Motion in Macaque Visual Cortex" [Slides]

- Inmar Givoni, "Semi-supervised Affinity Propagation" [Slides]

- Renqiang Min, "Kernel Learning Using Neural Networks" [Slides]

- Olivier Delalleau, "Parallel Stochastic Gradient Descent" [Slides]

- David F. Nichols, "Comparison of interocular suppression for binocular rivalry and flash suppression" [Slides]