Saturday Morning Videos: Representation Learning Workshop at Simons Institute, Berkeley (March 27th-31st, 2017)

 

A photo of New Zealand taken this week. 

 ICLR is in three weeks, so what about dwelving into representation learning. As it turns out, this week, the Representation Learning Workshop took place at the Simons Institute for the Theory of Computation at UC Berkeley. Here are all the videos (an impressive feat given it happened this week). Thank you to the organizers Sham Kakade , Sanjeev Arora , Kristen Grauman, Ruslan Salakhutdinov, Noah Smith  for the line-up. 

Here is the abstract:

This workshop will focus on dramatic advances in representation and learning taking place in natural language processing, speech and vision.  For instance, deep learning can be thought of as a method that combines the tasks of finding a classifier (which we can think of as the top layer of the deep net) with the task of learning a representation (namely, the representation computed at the last-but-one layer).

Developing a theory for such empirical work is an exciting quest, especially since the empirical work draws upon non-convex optimization.  The workshop will draw a mix of theorists and practitioners, and the following is a list of sample issues that will be discussed:  (a) Which models for representation make more sense than
others, and why? (In other words, what patterns in data are they capturing, and how are those patterns useful?)  (b) What is an analog of generalization theory for representation learning?  Can it lead to a theory of transfer learning to new distributions of inputs?  (c) How can we design algorithms for representation learning with provable guarantees?  What progress has already been made, and what lessons can we draw from it?  (d) How can we learn representations that combine probabilities and logic?

and the videos (Russ's slides are also added per his tweet) . Enjoy !

Monday, March 27th, 2017

9:00 am – 9:20 am		Coffee and Check-In
9:20 am – 9:30 am		Opening Remarks
9:30 am – 10:10 am		Geometry, Optimization and Generalization in Multilayer Networks Nathan Srebro, TTI Chicago
10:10 am – 10:50 am		A Non-generative Framework and Convex Relaxations for Unsupervised Learning Elad Hazan, Princeton University
10:50 am – 11:20 am		Break
11:20 am – 12:20 pm		Representations for Language: From Word Embeddings to Sentence Meanings ( slides ) Christopher Manning, Stanford University
12:20 pm – 2:15 pm		Lunch
2:15 pm – 3:15 pm		The Missing Signal Leon Bottou, Facebook AI Research
3:15 pm – 3:45 pm		Break
3:45 pm – 4:25 pm		Representations of Relationships in Images and Text Hal Daume, University of Maryland at College Park
4:30 pm – 4:45 pm		Spotlight Talk: A Formalization of Representation Learning Andrej Risteski, Princeton University
4:45 pm – 5:00 pm		Spotlight Talk: Semi-Random Units for Learning Neural Networks with Guarantees Bo Xie, Georgia Institute of Technology
5:00 pm – 6:00 pm		Reception

Tuesday, March 28th, 2017

9:00 am – 9:30 am		Coffee and Check-In
9:30 am – 10:10 am		Unsupervised Discovery Through Adversarial Self-Play Rob Fergus, New York University
10:10 am – 10:50 am		Learning from Unlabeled Video Kristen Grauman, University of Texas, Austin
10:50 am – 11:20 am		Break
11:20 am – 12:20 pm		Deep Reinforcement Learning Pieter Abbeel, UC Berkeley
12:20 pm – 2:15 pm		Lunch
2:15 pm – 2:55 pm		Supersizing Self-Supervision: Learning Perception and Action without Human Supervision Abhinav Gupta, Carnegie Mellon University
2:55 pm – 3:35 pm		Failures of Deep Learning Shai Shalev-Shwartz, Hebrew University of Jerusalem
3:35 pm – 4:10 pm		Break
4:10 pm – 5:00 pm		Panel

Wednesday, March 29th, 2017

9:00 am – 9:30 am		Coffee and Check-In
9:30 am – 10:10 am		Continuous State Machines and Grammars for Linguistic Structure Prediction Noah Smith, University of Washington
10:10 am – 10:50 am		Adversarial Perceptual Representation Learning Across Diverse Modalities and Domains Trevor Darrell, UC Berkeley
10:50 am – 11:20 am		Break
11:20 am – 12:20 pm		Evaluating Neural Network Representations Against Human Cognition Tom Griffiths, UC Berkeley
12:20 pm – 2:15 pm		Lunch
2:15 pm – 2:55 pm		Representation Learning for Reading Comprehension ( slides ) Russ Salakhutdinov, Carnegie Mellon University
2:55 pm – 3:35 pm		Tractable Learning in Structured Probability Spaces Adnan Darwiche, UCLA
3:35 pm – 4:10 pm		Break
4:10 pm – 4:30 pm		Spotlight Talk: Convolutional Dictionary Learning through Tensor Factorization Furong Huang, UC Irvine
4:35 pm – 4:55 pm		Spotlight Talk: How to Escape Saddle Points Efficiently Praneeth Netrapalli, Microsoft Research India

Thursday, March 30th, 2017

9:00 am – 9:30 am		Coffee and Check-In
9:30 am – 10:10 am		Re-Thinking Representational Learning in Robotics and Music Sham Kakade, University of Washington
10:10 am – 10:50 am		Representation Learning of Grounded Language and Knowledge: with and without End-to-End Learning Yejin Choi, University of Washington
10:50 am – 11:20 am		Break
11:20 am – 12:20 pm		TBD Yann LeCun, New York University
12:20 pm – 2:15 pm		Lunch
2:15 pm – 2:55 pm		Generalization and Equilibrium in Generative Adversarial Nets (GANs) Sanjeev Arora, Princeton University
2:55 pm – 3:35 pm		Provably Learning of Noisy-or Networks Rong Ge, Duke University
3:35 pm – 4:10 pm		Break
4:10 pm – 5:00 pm		Panel

Friday, March 31st, 2017

9:00 am – 9:30 am		Coffee and Check-In
9:30 am – 10:10 am		Learning Paraphrastic Representations of Natural Language Sentences Kevin Gimpel, TTI Chicago
10:10 am – 10:50 am		Formation and Association of Symbolic Memories in the Brain Christos Papadimitriou, UC Berkeley
10:50 am – 11:20 am		Break
11:20 am – 12:20 pm		Learning Representations for Active Vision Bruno Olshausen, UC Berkeley
12:20 pm – 2:15 pm		Lunch
2:15 pm – 2:55 pm		Resilient Representation and Provable Generalization Dawn Song, UC Berkeley
2:55 pm – 3:35 pm		Word Representation Learning without unk Assumptions Chris Dyer, Carnegie Mellon University
3:35 pm – 4:10 pm		Break
4:10 pm – 4:30 pm		Spotlght Talk: Performance Guarantees for Transferring Representations Daniel McNamara, Australian National University

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