Deep Learning Summer School 2016 - Montreal

Deep Learning Summer School, Montreal 2016

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 2016 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.

Categories

Top » Computer Science » Machine Learning » Deep Learning
Top » Computer Science » Machine Learning » Unsupervised Learning
Top » Computer Science » Machine Learning » Reinforcement Learning


Invited Talks
[syn] 51536 views, 1:30:40 Invited Talk Machine LearningMachine Learning Doina Precup Doina Precup 9 comments	[syn] 32201 views, 2:44:19 Invited Talk Neural NetworksNeural Networks Hugo Larochelle Hugo Larochelle 2 comments	[syn] 7883 views, 1:28:14 Invited Talk Introduction to TheanoIntroduction to Theano Pascal Lamblin Pascal Lamblin	[syn] 16905 views, 1:23:21 Invited Talk Convolutional Neural Networks and Computer VisionConvolutional Neural Networks and Computer Vision Rob Fergus Rob Fergus 1 comment	[syn] 9976 views, 1:13:54 Invited Talk Learning to SeeLearning to See Antonio Torralba Antonio Torralba 1 comment
[syn] 5544 views, 55:36 Invited Talk Introduction to TorchIntroduction to Torch Alex Wiltschko Alex Wiltschko	[syn] 42585 views, 1:22:49 Invited Talk Recurrent Neural NetworksRecurrent Neural Networks Yoshua Bengio Yoshua Bengio 4 comments	[syn] 13306 views, 1:20:51 Invited Talk Reasoning, Attention and MemoryReasoning, Attention and Memory Sumit Chopra Sumit Chopra	[syn] 21620 views, 2:00:37 Invited Talk Large Scale Deep Learning with TensorFlowLarge Scale Deep Learning with TensorFlow Jeffrey Dean Jeffrey Dean 1 comment	[syn] 20386 views, 1:20:43 Invited Talk Deep Natural Language UnderstandingDeep Natural Language Understanding Kyunghyun Cho Kyunghyun Cho 3 comments
[syn] 20665 views, 1:25:34 Invited Talk Beyond Seq2Seq with Augmented RNNsBeyond Seq2Seq with Augmented RNNs Edward Grefenstette Edward Grefenstette	[syn] 5840 views, 55:10 Invited Talk GPU programming for Deep LearningGPU programming for Deep Learning Julie Bernauer, Ryan Olson Julie Bernauer, Ryan Olson	[syn] 48142 views, 1:13:31 Invited Talk Introduction to Reinforcement LearningIntroduction to Reinforcement Learning Joelle Pineau Joelle Pineau 6 comments	[syn] 38380 views, 1:51:07 Invited Talk Deep Reinforcement LearningDeep Reinforcement Learning Pieter Abbeel Pieter Abbeel 4 comments	[syn] 5110 views, 30:14 Invited Talk Advanced Topics in RLAdvanced Topics in RL Doina Precup Doina Precup 1 comment
[syn] 4767 views, 15:24 Invited Talk Advanced Topics in RLAdvanced Topics in RL Joelle Pineau Joelle Pineau	[syn] 14382 views, 1:27:55 Invited Talk Learning Deep Generative ModelsLearning Deep Generative Models Ruslan Salakhutdinov Ruslan Salakhutdinov	[syn] 23262 views, 1:19:14 Invited Talk Building Machines that Imagine and Reason: Principles and Applications of Deep Generative ModelsBuilding Machines that Imagine and Reason: Principles and Applications of ... Shakir Mohamed Shakir Mohamed 1 comment	[syn] 5740 views, 1:27:26 Invited Talk Beyond inspiration: Five lessons from biology on building intelligent machinesBeyond inspiration: Five lessons from biology on building intelligent machines Bruno Olshausen Bruno Olshausen	[syn] 9421 views, 1:33:00 Invited Talk Theoretical neuroscience and deep learning theoryTheoretical neuroscience and deep learning theory Surya Ganguli Surya Ganguli
Contributed Talks
[syn] 4227 views, 12:55 Chains of Reasoning over Entities, Relations, and Text using Recurrent Neural NetworksChains of Reasoning over Entities, Relations, and Text using Recurrent ... Rajarshi Das Rajarshi Das	[syn] 7195 views, 14:29 Professor Forcing: A New Algorithm for Training Recurrent NetworksProfessor Forcing: A New Algorithm for Training Recurrent Networks Anirudh Goyal Anirudh Goyal	[syn] 5848 views, 10:59 Zoneout: Regularizing RNNs by Randomly Preserving Hidden ActivationsZoneout: Regularizing RNNs by Randomly Preserving Hidden Activations Tegan Maharaj Tegan Maharaj	[syn] 2851 views, 18:58 Deep multi-view representation learning of brain responses to natural stimuliDeep multi-view representation learning of brain responses to natural stimuli Leila Wehbe Leila Wehbe	[syn] 7788 views, 14:49 Learning to Communicate with Deep Multi-Agent Reinforcement LearningLearning to Communicate with Deep Multi-Agent Reinforcement Learning Jakob Foerster Jakob Foerster
[syn] 2302 views, 13:57 Model-Based Relative Entropy Stochastic SearchModel-Based Relative Entropy Stochastic Search Abbas Abdolmaleki Abbas Abdolmaleki	[syn] 2021 views, 16:33 Learning Nash Equilibrium for General-Sum Markov Games from Batch DatLearning Nash Equilibrium for General-Sum Markov Games from Batch Dat Julien Pérolat Julien Pérolat	[syn] 4629 views, 20:30 A Network-based End-to-End Trainable Task-oriented Dialogue SystemA Network-based End-to-End Trainable Task-oriented Dialogue System Tsung-Hsien Wen Tsung-Hsien Wen	[syn] 3008 views, 15:28 Inference LearningInference Learning Patrick Putzky Patrick Putzky	[syn] 5362 views, 16:45 Variational Autoencoders with PixelCNN DecodersVariational Autoencoders with PixelCNN Decoders Ishaan Gulrajani Ishaan Gulrajani
[syn] 2677 views, 13:33 An Infinite Restricted Boltzmann MachineAn Infinite Restricted Boltzmann Machine Marc-Alexandre Côté Marc-Alexandre Côté	[syn] 2988 views, 15:15 Deep siamese neural network for prediction of long-range interactions in chromatinDeep siamese neural network for prediction of long-range interactions in ... Davide Chicco Davide Chicco	[syn] 3056 views, 14:09 Beam Search Message Passing in Bidirectional RNNs: Applications to Fill-in-the-Blank Image CaptioningBeam Search Message Passing in Bidirectional RNNs: Applications to Fill-in-the-Blank ... Qing Sun Qing Sun	[syn] 6069 views, 18:40 Analyzing the Behavior of Deep Visual Question Answering ModelsAnalyzing the Behavior of Deep Visual Question Answering Models Aishwarya Agrawal Aishwarya Agrawal	[syn] 3018 views, 13:55 Recombinator Networks: Learning Coarse-to-Fine Feature AggregationRecombinator Networks: Learning Coarse-to-Fine Feature Aggregation Sina Honari Sina Honari

Reviews and comments:

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4 Lola Pickett, December 1, 2020 at 4:38 p.m.:

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8 David Farley, February 14, 2021 at 1:51 p.m.:

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18 EssayUK, December 22, 2021 at 9:17 a.m.:

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19 Philip, January 16, 2022 at 5:29 p.m.:

summer schools is aimed at graduate students and industrial engineers and researchers who already have some basic knowledge of machine learning.

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20 Kinsey Hotchner, January 25, 2022 at 2:50 p.m.:

Good school, I studied there in the summer, I recommend it.
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Deep Learning Summer School 2016 - Montreal

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