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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