Nytro Posted August 31, 2017 Report Share Posted August 31, 2017 Deep Learning (DLSS) and Reinforcement Learning (RLSS) Summer School, Montreal 2017 Deep neural networks that learn to represent data in multiple layers of increasing abstraction have dramatically improved the state-of-the-art for speech recognition, object recognition, object detection, predicting the activity of drug molecules, and many other tasks. Deep learning discovers intricate structure in large datasets by building distributed representations, either via supervised, unsupervised or reinforcement learning. The Deep Learning Summer School (DLSS) is aimed at graduate students and industrial engineers and researchers who already have some basic knowledge of machine learning (and possibly but not necessarily of deep learning) and wish to learn more about this rapidly growing field of research. In collaboration with DLSS we will hold the first edition of the Montreal Reinforcement Learning Summer School (RLSS). RLSS will cover the basics of reinforcement learning and show its most recent research trends and discoveries, as well as present an opportunity to interact with graduate students and senior researchers in the field. The school is intended for graduate students in Machine Learning and related fields. Participants should have advanced prior training in computer science and mathematics, and preference will be given to students from research labs affiliated with the CIFAR program on Learning in Machines and Brains. Deep Learning Summer School [syn] 630 views, 1:26:30 Machine Learning Doina Precup [syn] 223 views, 3:03:15 Neural Networks Hugo Larochelle [syn] 320 views, 1:25:47 Recurrent Neural Networks (RNNs) Yoshua Bengio [syn] 81 views, 1:30:25 Probabilistic numerics for deep learning Michael Osborne [syn] 211 views, 1:18:03 Generative Models I Ian Goodfellow 34 views, 34:51 Theano Pascal Lamblin [syn] 42 views, 1:05:58 AI Impact on Jobs Michael Osborne [syn] 71 views, 1:28:54 Introduction to CNNs Richard Zemel 1:28:22 Structured Models/Advanced Vision Raquel Urtasun [syn] 177 views, 55:15 Torch/PyTorch Soumith Chintala [syn] 48 views, 1:28:25 Generative Models II Aaron Courville [syn] 81 views, 1:24:30 Natural Language Understanding Phil Blunsom [syn] 40 views, 1:23:42 Natural Language Processing Phil Blunsom 62 views, 15:25 Bayesian Hyper Networks David Scott Krueger 14:01 Gibs Net Alex Lamb 196 views, 12:23 Pixel GAN autoencoder Alireza Makhzani 48 views, 16:16 CRNN's Rémi Leblond, Jean-Baptiste Alayrac [syn] 94 views, 1:23:34 Deep learning in the brain Blake Aaron Richards [syn] 70 views, 1:32:38 Theoretical Neuroscience and Deep Learning Theory Surya Ganguli [syn] 108 views, 1:23:14 Marrying Graphical Models & Deep Learning Max Welling 127 views, 1:21:05 Learning to Learn Nando de Freitas [syn] 36 views, 1:18:12 Automatic Differentiation Matthew James Johnson [syn] 54 views, 1:30:25 Combining Graphical Models and Deep Learning Matthew James Johnson 24 views, 12:52 Domain Randomization for Cuboid Pose Estimation Jonathan Tremblay 21 views, 15:48 tbd Rogers F. Silva 106 views, 16:26 What Would Shannon Do? Bayesian Compression for DL Karen Ullrich 21 views, 13:13 On the Expressive Efficiency of Overlapping Architectures of Deep Learning Or Sharir Reinforcement Learning Summer School 185 views, 1:29:32 Reinforcement Learning Joelle Pineau 84 views, 1:28:26 Policy Search for RL Pieter Abbeel 122 views, 1:26:24 TD Learning Richard S. Sutton 55 views, 1:21:20 Deep Reinforcement Learning Hado van Hasselt 79 views, 1:23:52 Deep Control Nando de Freitas 47 views, 1:23:58 Theory of RL Csaba Szepesvári [syn] 35 views, 1:29:02 Reinforcement Learning Satinder Singh 25 views, 1:21:44 Safe RL Philip S. Thomas 35 views, 43:54 Applications of bandits and recommendation systems Nicolas Le Roux 71 views, 1:02:35 Cooperative Visual Dialogue with Deep RL Devi Parikh, Dhruv Batra Sursa: http://videolectures.net/deeplearning2017_montreal/ Quote Link to comment Share on other sites More sharing options...