In episode 80 of The Gradient Podcast, Daniel Bashir speaks to Professor Hugo Larochelle.

Professor Larochelle leads the Montreal Google DeepMind team and is adjunct professor at Université de Montréal and a Canada CIFAR Chair. His research focuses on the study and development of deep learning algorithms.

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Outline:

* (00:00) Intro

* (01:38) Prof. Larochelle’s background, working in Bengio’s lab

* (04:53) Prof. Larochelle’s work and connectionism

* (08:20) 2004-2009, work with Bengio

* (08:40) Nonlocal Estimation of Manifold Structure, manifolds and deep learning

* (13:58) Manifold learning in vision and language

* (16:00) Relationship to Denoising Autoencoders and greedy layer-wise pretraining

* (21:00) From input copying to learning about local distribution structure

* (22:30) Zero-Data Learning of New Tasks

* (22:45) The phrase “extend machine learning towards AI” and terminology

* (26:55) Prescient hints of prompt engineering

* (29:10) Daniel goes on totally unnecessary tangent

* (30:00) Methods for training deep networks (strategies and robust interdependent codes)

* (33:45) Motivations for layer-wise pretraining

* (35:15) Robust Interdependent Codes and interactions between neurons in a single network layer

* (39:00) 2009-2011, postdoc in Geoff Hinton’s lab

* (40:00) Reflections on the AlexNet moment

* (41:45) Frustration with methods for evaluating unsupervised methods, NADE

* (44:45) How researchers thought about representation learning, toying with objectives instead of architectures

* (47:40) The Restricted Boltzmann Forest

* (50:45) Imposing structure for tractable learning of distributions

* (53:11) 2011-2016 at U Sherbooke (and Twitter)

* (53:45) How Prof. Larochelle approached research problems

* (56:00) How Domain Adversarial Networks came about

* (57:12) Can we still learn from Restricted Boltzmann Machines?

* (1:02:20) The ~ Infinite ~ Restricted Boltzmann Machine

* (1:06:55) The need for researchers doing different sorts of work

* (1:08:58) 2017-present, at MILA (and Google)

* (1:09:30) Modulating Early Visual Processing by Language, neuroscientific inspiration

* (1:13:22) Representation learning and generalization, what is a good representation (Meta-Dataset, Universal representation transformer layer, universal template, Head2Toe)

* (1:15:10) Meta-Dataset motivation

* (1:18:00) Shifting focus to the problem—good practices for “recycling deep learning”

* (1:19:15) Head2Toe intuitions

* (1:21:40) What are “universal representations” and manifold perspective on datasets, what is the right pretraining dataset

* (1:26:02) Prof. Larochelle’s takeaways from Fortuitous Forgetting in Connectionist Networks (led by Hattie Zhou)

* (1:32:15) Obligatory commentary on The Present Moment and current directions in ML

* (1:36:18) The creation and motivations of the TMLR journal

* (1:41:48) Prof. Larochelle’s takeaways about doing good science, building research groups, and nurturing a research environment

* (1:44:05) Prof. Larochelle’s advice for aspiring researchers today

* (1:47:41) Outro

Links:

* Professor Larochelle’s homepage and Twitter

* Transactions on Machine Learning Research

* Papers

* 2004-2009

* Nonlocal Estimation of Manifold Structure

* Classification using Discriminative Restricted Boltzmann Machines

* Zero-data learning of new tasks

* Exploring Strategies for Training Deep Neural Networks

* Deep Learning using Robust Interdependent Codes

* 2009-2011

* Stacked Denoising Autoencoders

* Tractable multivariate binary density estimation and the restricted Boltzmann forest

* The Neural Autoregressive Distribution Estimator

* Learning Attentional Policies for Tracking and Recognition in Video with Deep Networks

* 2011-2016

* Practical Bayesian Optimization of Machine Learning Algorithms

* Learning Algorithms for the Classification Restricted Boltzmann Machine

* A neural autoregressive topic model

* Domain-Adversarial Training of Neural Networks

* NADE

* An Infinite Restricted Boltzmann Machine

* 2017-present

* Modulating early visual processing by language

* Meta-Dataset

* A Universal Representation Transformer Layer for Few-Shot Image Classification

* Learning a universal template for few-shot dataset generalization

* Impact of aliasing on generalization in deep convolutional networks

* Head2Toe: Utilizing Intermediate Representations for Better Transfer Learning

* Fortuitous Forgetting in Connectionist Networks

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