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]