The Impact of Conferences on Publication Culture

TMLR is a journal that aims to push publication culture and all in a better direction. One of the motivations for doing tmlr was definitely that I having been involved in conferences quite a bit it became apparent to me that conferences were kind of playing this dual mission which we're often confusing. TMLR also wants to try to have a process that really assesses our claims matched by proper convincing evidence and then deal with okay but amongst all of this work what is most exciting knowing that this is editorial this is subjective and finding ways so in terms of the way that we're trying to do this is to have certifications that sort of highlights papers among those that are accepted.

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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