Graduate Summer School: Deep Learning, Feature Learning

July 9 - 27, 2012

Schedule


Monday, July 9, 2012

9:00 - 10:00 Geoffrey Hinton (University of Toronto)

PART 1: Introduction to Deep Learning & Deep Belief Nets (PDF Parts 1 & 2)
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10:30 - 11:30 Geoffrey Hinton (University of Toronto)

PART 2: Using backpropagation for fine-tuning a generative model to be better at discrimination
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2:30 - 3:30 Yann LeCun (New York University)

Deep Learning, Graphical Models, Energy-Based Models, Structured Prediction
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4:00 - 5:00 Yann LeCun (New York University)

Deep Learning, Graphical Models, EnergyBased Models, Structured Prediction (Part 2)
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Tuesday, July 10, 2012

9:00 - 10:00 Andrew Ng (Stanford University)

Deep Learning, Self-Taught Learning and Unsupervised Feature Learning (Part 1 Slides1-68; Part 2 Slides 69-109)
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10:30 - 11:30 Andrew Ng (Stanford University)

Advanced topics + Research philosophy / Neural Networks: Representation
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12:00 - 1:00 Andrew Ng (Stanford University)

Non-linear hypotheses
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2:30 - 3:30 Andrew Ng (Stanford University)

Non-linear hypotheses (Part 2)
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Wednesday, July 11, 2012

9:00 - 10:00 Yann LeCun (New York University)

Learning Hierarchies of Invariant Features (Parts 1 & 2)
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10:30 - 11:30 Yann LeCun (New York University)

Deep Learning, Graphical Models, EnergyBased Models, Structured Prediction (Part 3)
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2:30 - 3:30 Geoffrey Hinton (University of Toronto)

PART 3: Some applications of deep learning (Slides 1-38)
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4:00 - 5:00 Geoffrey Hinton (University of Toronto)

PART 4: A computational principle that explains sex, the brain, and sparse coding (Slides 39-92)
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Thursday, July 12, 2012

9:00 - 10:00 Rob Fergus (New York University)

Deep Learning Methods for Vision
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10:30 - 11:30 Rob Fergus (New York University)

Deep Learning Methods for Vision (Part 2)
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12:00 - 1:00 Geoffrey Hinton (University of Toronto)

 
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2:30 - 3:30 Alan Yuille (University of California, Los Angeles (UCLA))

Part 1
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Friday, July 13, 2012

9:00 - 10:00 Graham Taylor (University of Guelph)

 
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10:30 - 11:30 Graham Taylor (University of Guelph)

 
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12:00 - 1:00 Alan Yuille (University of California, Los Angeles (UCLA))

Part 2
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2:30 - 3:30 Alan Yuille (University of California, Los Angeles (UCLA))

Part 3
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4:00 - 5:00 Yann LeCun (New York University)

Deep Learning, Graphical Models, EnergyBased Models, Structured Prediction (Part 4)
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Monday, July 16, 2012

9:00 - 10:00 Yoshua Bengio (University of Montreal)

Part 1
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10:30 - 11:30 Yoshua Bengio (University of Montreal)

Part 2
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12:00 - 1:00 Stephen Wright (University of Wisconsin-Madison)

Some Relevant Topics in Optimization (Part 1) - (Slides Cover Parts 1 & 2)
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2:30 - 3:30 Stephen Wright (University of Wisconsin-Madison)

Some Relevant Topics in Optimization (Part 2)
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4:00 - 5:00 Arthur Szlam (New York University)

A tutorial on sparse modeling.
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Tuesday, July 17, 2012

9:00 - 10:00 Stephen Wright (University of Wisconsin-Madison)

Sparse and Regularized Optimization Part 1 (Slides Cover Parts 1 & 2)
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10:30 - 11:30 Stephen Wright (University of Wisconsin-Madison)

Sparse and Regularized Optimization Part 2
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12:00 - 1:00 Arthur Szlam (New York University)

 
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2:30 - 3:30 Yoshua Bengio (University of Montreal)

Part 3
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4:00 - 5:00 Yoshua Bengio (University of Montreal)

Part 4
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Wednesday, July 18, 2012

9:00 - 10:00 Stéphane Mallat (École Polytechnique)

Scattering Invariant Deep Networks for Classification
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10:30 - 11:30 Stéphane Mallat (École Polytechnique)

Scattering Invariant Deep Networks for Classification (Part 2)
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12:00 - 1:00 Kai Yu (Baidu Inc.)

Part 1
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2:30 - 3:30 Kai Yu (Baidu Inc.)

Part 2
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4:00 - 5:00 Jorge Nocedal (Northwestern University)

Tutorial on Optimization methods for machine learning
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Thursday, July 19, 2012

9:00 - 10:00 Jorge Nocedal (Northwestern University)

Tutorial on Optimization methods for machine learning (Part 2)
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10:30 - 11:30 Jorge Nocedal (Northwestern University)

Tutorial on Optimization methods for machine learning (Part 3)
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2:00 - 3:30 Stéphane Mallat (École Polytechnique)

Scattering Invariant Deep Networks for Classification (Part 3)
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4:00 - 5:00 Jason Morton (Pennsylvania State University)

An Algebraic Perspective on Deep Learning
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Friday, July 20, 2012

9:00 - 10:00 Jason Morton (Pennsylvania State University)

An Algebraic Perspective on Deep Learning (Part 2)
PDF Presentation
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10:30 - 11:30 Jason Morton (Pennsylvania State University)

An Algebraic Perspective on Deep Learning (Part 3)
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12:00 - 1:00 Yoshua Bengio (University of Montreal)

 
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2:30 - 3:30 Stanley Osher (University of California, Los Angeles (UCLA))

 
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4:00 - 5:00 Stanley Osher (University of California, Los Angeles (UCLA))

 
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Monday, July 23, 2012

9:00 - 10:00 Ruslan Salakhutdinov (University of Toronto)

 
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10:30 - 11:30 Ruslan Salakhutdinov (University of Toronto)

 
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12:00 - 1:00 Marc'Aurelio Ranzato (Google Inc.)

Deep Gated MRF's - part 1
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2:30 - 3:30 Marc'Aurelio Ranzato (Google Inc.)

Deep Gated MRF's - part 2
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4:00 - 5:00 Jason Weston (Google Research)

Part 1
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6:30 - 7:30 Brian Milch (Google Inc.)

From Text to Concepts at Google


Tuesday, July 24, 2012

9:00 - 10:00 Jason Weston (Google Research)

Part 2
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10:30 - 11:30 Jason Weston (Google Research)

Part 3
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12:00 - 1:00 Ruslan Salakhutdinov (University of Toronto)

 
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2:30 - 3:30 Bruno Olshausen (University of California, Berkeley (UC Berkeley))

From natural scene statistics to models of neural coding and representation (part 1)
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4:00 - 5:00 Marc'Aurelio Ranzato (Google Inc.)

Large Scale Deep Learning
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Wednesday, July 25, 2012

9:00 - 10:00 Thomas Serre (Brown University)

Deep learning in the visual cortex - Part 1
PDF Presentation
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10:30 - 11:30 Thomas Serre (Brown University)

Deep learning in the visual cortex - Part 2
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12:00 - 1:00 Roland Memisevic (Johann Wolfgang Goethe-Universität Frankfurt)

Multiview Feature Learning - Part 1
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2:30 - 3:30 Roland Memisevic (Johann Wolfgang Goethe-Universität Frankfurt)

Multiview Feature Learning - Part 2
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4:00 - 5:00 Bruno Olshausen (University of California, Berkeley (UC Berkeley))

From natural scene statistics to models of neural coding and representation (part 2)
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Thursday, July 26, 2012

9:00 - 10:00 Iain Murray (University of Edinburgh)

Introduction to MCMC for deep learning
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10:30 - 11:30 Iain Murray (University of Edinburgh)

Density estimation
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12:00 - 1:00 Nando de Freitas (University of British Columbia)

Part 1
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2:30 - 3:30 Nando de Freitas (University of British Columbia)

Part 2
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4:00 - 5:00 Thomas Serre (Brown University)

Deep learning in the visual cortex - Part 3
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Friday, July 27, 2012

9:00 - 10:00 Nando de Freitas (University of British Columbia)

An Informal Mathematical Tour of Feature Learning
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10:30 - 11:30 Nando de Freitas (University of British Columbia)

Part 3
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