## CIFAR
NCAP - Summer School 2014
Tuesday,
August 12, 2014 - Saturday, August 16, 2014 Location:
University of Toronto Organizers:
Geoffrey Hinton and
Yoshua Bengio Neural Computation & Adaptive
Perception aims to unlock the mystery of how our brains convert sensory stimuli
into information and to recreate human-style learning in computers. While
this group focuses on visual systems, their research also points to broader
explanations of how the brain processes other kinds of important information,
including sounds, smells and tastes. ## Presentations and Slides:
1.
Deep Learning by Yoshua Bengio (slides) 2.
Non-Gaussian Statistics and Natural Images by Aapo Hyvärinen (slides-1, slides-2) 3.
Topics in Deep Learning: Language, Imitation and Automatic
Configuration by Nando de Freitas
(slides-1, slides-2) 4.
Compressed Sensing and Natural Image Statistics by Yair Weiss (slides-1, slides-2) 5.
Sparse Coding in Brains and Machines by Bruno Olshausen (slides) 6.
Unsolved Problems in Vision by Bruno Olshausen
(slides)
1.
Generating Sequences with Recurrent Neural Networks by
Alex Graves (slides) 2.
Machine Translation with LSTMs by Ilya
Sutskever (slides) 3.
Using Bayesian Optimization to Optimize Machine Learning
by Ryan Adams (slides) 4.
The Geometry of RBM Training and Evaluation by Roger
Grosse (slides) 5.
Dark Knowlege by Geoffrey
Hinton (slides-pptx, slides-pdf)
1.
An Autoencoder Approach to
Learning Bilingual Word Representations By Stanislas
Lauly (slides) 2.
Critical Points in High Dimensional Landscapes by Levent Sagun (slides) 3.
Real Time Occlusion Boundary Detection from Video by Donglai Wei (slides) 4.
Learning the Local Statistics of Optical Flow by Dan
Rosenbaum (slides) 5.
Patch Statistics for Natural Image Decompositions by Ofer Springer (slides) 6.
Masked Autoencoder for
Distribution Estimation (MADE) by Mathieu Germain (slides) 7.
Learning to Discover Efficient Mathematical Identities by Wojciech Zaremba (slides) 8.
Generative Models for Sentences by Amjad
Almahairi (slides) 9.
Many Paths to Computing Normalizing Constants by Yuri Burda (slides) 10.
Impact of Attention on Perception by Ashkan
Amiri (slides) 11.
Neural Machine Translation: Approaches, Challenges and By-Products by Kyunghyun
Cho (slides) 12.
Training Neural Bayesian Nets by Laurent
Dinh (slides) 13.
Pixels to Voxels: Modelling Visual Representations in the Human Brain by Pulkit
Agrawal (slides) 14.
Error Correction as Data Modeling in Emerging Memory Technologies by Jesse
Engel (slides) 15.
Time-series Structure in Innate Behavior by Alex
Wiltschko (slides) 16.
Multimodal Learning with Deep Boltzmann Machines by Nitish
Srivastava (slides) 17.
Balancing Neural Networks by Brian
Cheung (slides) 18.
Generating image captions with neural networks by Ryan
Kiros (slides) 19.
Learning β in β-NMF by José
Sotelo (slides) 20.
Quantization of Image Features by Roni
Feldman (slides) 21.
Combining Modality Specific Deep Neural Networks for Emotion Recognition in Video by Samira
Ebrahimi Kahou (slides) 22.
Learning from Noisy Labels with Deep Neural Network by Sainbayar
Sukhbaatar (slides) 23.
Efficient Inference of Continuous Markov Random Fields with Polynomial Potentials by Shenlong
Wang (slides) 24.
Predicting Drug Response in Rheumatoid Arthritis Patients by Daniel
Hidru (slides) 25.
Learning Sensory Representations For Actions by Mayur
Mudigonda (slides) |