Portrait of Hugo Larochelle

Hugo Larochelle

Core Industry Member
Adjunct professor, Université de Montréal, Depatment of Computer Science and Operations Research
Research Scientist
Scientific Director, Leadership Team
Research Topics
Deep Learning
Google Scholar

Biography

Hugo Larochelle is a pioneering deep learning researcher, industry leader and philanthropist.

He started his academic journey with two of the « Godfathers » of artificial intelligence: Yoshua Bengio, his Ph.D. supervisor at the Université de Montréal, and Geoffrey Hinton, his postdoctoral supervisor at the University of Toronto.

Over the years, his research has contributed several conceptual breakthroughs found in modern AI systems. His work on Denoising Autoencoders (DAE) identified the reconstruction of clean data from corrupted versions as a scalable paradigm for learning meaningful representations from large quantities of unlabeled data. With models such as the Neural Autoregressive Distribution Estimator (NADE) and the Masked Autoencoder Distribution Estimator (MADE), he helped popularize autoregressive modeling with neural networks, a paradigm now omnipresent in generative AI. And his work on Zero-Data Learning of New Tasks introduced for the first time the now common concept of zero-shot learning.

He then brought his academic expertise to the industry by co-founding the startup Whetlab, which was acquired by Twitter in 2015. After a role at Twitter Cortex, he was recruited to lead Google's AI research lab in Montreal (Google Brain), now part of Google DeepMind. He is an Adjunct Professor at the Université de Montréal, mentoring the next generation of AI researchers. He has also developed a series of free online courses on machine learning.

A father of four, Hugo Larochelle and his wife, Angèle St-Pierre, have also made multiple donations to the Université de Montréal, Université de Sherbrooke (where he used to be a Professor) and Université Laval to support students and advance research, particularly in AI for environmental sustainability. He also initiated the TechAide conference, mobilizing Montreal's tech community to raise funds for the charity Centraide to support its mission to fight poverty and social exclusion.

Current Students

Sangnie Bhardwaj
PhD - Université de Montréal
Principal supervisor :
Guillaume Lajoie
Mélisande Astrid Crystal Teng
PhD - Université de Montréal
Principal supervisor :
Yoshua Bengio
Website
Github
Hattie Zhou
PhD - Université de Montréal
Co-supervisor :
Aaron Courville

Publications

HoME: a Household Multimodal Environment
Simon Brodeur
Ethan Perez
Ankesh Anand
Florian Golemo
Luca Celotti
Florian Strub
Jean Rouat
Hugo Larochelle
Aaron Courville
We introduce HoME: a Household Multimodal Environment for artificial agents to learn from vision, audio, semantics, physics, and interaction… (see more) with objects and other agents, all within a realistic context. HoME integrates over 45,000 diverse 3D house layouts based on the SUNCG dataset, a scale which may facilitate learning, generalization, and transfer. HoME is an open-source, OpenAI Gym-compatible platform extensible to tasks in reinforcement learning, language grounding, sound-based navigation, robotics, multi-agent learning, and more. We hope HoME better enables artificial agents to learn as humans do: in an interactive, multimodal, and richly contextualized setting.
2018-01-01
ICLR (Workshop) (published)
openreview.net
openreview.net
GuessWhat?! Visual Object Discovery through Multi-modal Dialogue
Harm de Vries
Florian Strub
Sarath Chandar
Olivier Pietquin
Hugo Larochelle
Aaron Courville
We introduce GuessWhat?!, a two-player guessing game as a testbed for research on the interplay of computer vision and dialogue systems. The… (see more) goal of the game is to locate an unknown object in a rich image scene by asking a sequence of questions. Higher-level image understanding, like spatial reasoning and language grounding, is required to solve the proposed task. Our key contribution is the collection of a large-scale dataset consisting of 150K human-played games with a total of 800K visual question-answer pairs on 66K images. We explain our design decisions in collecting the dataset and introduce the oracle and questioner tasks that are associated with the two players of the game. We prototyped deep learning models to establish initial baselines of the introduced tasks.
2017-07-21
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (published)
doi.org
arxiv.org
Modulating early visual processing by language
Harm de Vries
Florian Strub
Jérémie Mary
Hugo Larochelle
Olivier Pietquin
Aaron Courville
It is commonly assumed that language refers to high-level visual concepts while leaving low-level visual processing unaffected. This view do… (see more)minates the current literature in computational models for language-vision tasks, where visual and linguistic input are mostly processed independently before being fused into a single representation. In this paper, we deviate from this classic pipeline and propose to modulate the \emph{entire visual processing} by linguistic input. Specifically, we condition the batch normalization parameters of a pretrained residual network (ResNet) on a language embedding. This approach, which we call MOdulated RESnet (\MRN), significantly improves strong baselines on two visual question answering tasks. Our ablation study shows that modulating from the early stages of the visual processing is beneficial.
2017-07-02
ArXiv (preprint)
arxiv.org
arxiv.org
Movie Description
Anna Rohrbach
Atousa Torabi
Marcus Rohrbach
Niket Tandon
Chris Pal
Hugo Larochelle
Aaron Courville
Bernt Schiele
2017-01-25
International Journal of Computer Vision (published)
doi.org
arxiv.org
Brain tumor segmentation with Deep Neural Networks
Mohammad Havaei
Axel Davy
David Warde-Farley
Antoine Biard
Aaron Courville
Yoshua Bengio
Chris Pal
Pierre-Marc Jodoin
Hugo Larochelle
2017-01-01
Medical Image Analysis (published)
doi.org
arxiv.org
Modulating early visual processing by language
Harm de Vries
Florian Strub
Jérémie Mary
Hugo Larochelle
Olivier Pietquin
Aaron Courville
It is commonly assumed that language refers to high-level visual concepts while leaving low-level visual processing unaffected. This view do… (see more)minates the current literature in computational models for language-vision tasks, where visual and linguistic input are mostly processed independently before being fused into a single representation. In this paper, we deviate from this classic pipeline and propose to modulate the \emph{entire visual processing} by linguistic input. Specifically, we condition the batch normalization parameters of a pretrained residual network (ResNet) on a language embedding. This approach, which we call MOdulated RESnet (\MRN), significantly improves strong baselines on two visual question answering tasks. Our ablation study shows that modulating from the early stages of the visual processing is beneficial.
arxiv.org
Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations
David Scott Krueger
Tegan Maharaj
J'anos Kram'ar
Mohammad Pezeshki
Nicolas Ballas
Nan Rosemary Ke
Anirudh Goyal
Yoshua Bengio
Hugo Larochelle
Aaron Courville
Chris Pal
We propose zoneout, a novel method for regularizing RNNs. At each timestep, zoneout stochastically forces some hidden units to maintain thei… (see more)r previous values. Like dropout, zoneout uses random noise to train a pseudo-ensemble, improving generalization. But by preserving instead of dropping hidden units, gradient information and state information are more readily propagated through time, as in feedforward stochastic depth networks. We perform an empirical investigation of various RNN regularizers, and find that zoneout gives significant performance improvements across tasks. We achieve competitive results with relatively simple models in character- and word-level language modelling on the Penn Treebank and Text8 datasets, and combining with recurrent batch normalization yields state-of-the-art results on permuted sequential MNIST.
2016-06-03
ArXiv (preprint)
arxiv.org
arxiv.org
Movie Description
Anna Rohrbach
Atousa Torabi
Marcus Rohrbach
Niket Tandon
Chris Pal
Hugo Larochelle
Aaron Courville
Bernt Schiele
2016-05-12
ArXiv (preprint)
doi.org
arxiv.org
Movie Description
Anna Rohrbach
Atousa Torabi
Marcus Rohrbach
Niket Tandon
Chris Pal
Hugo Larochelle
Aaron Courville
Bernt Schiele
Audio description (AD) provides linguistic descriptions of movies and allows visually impaired people to follow a movie along with their pee… (see more)rs. Such descriptions are by design mainly visual and thus naturally form an interesting data source for computer vision and computational linguistics. In this work we propose a novel dataset which contains transcribed ADs, which are temporally aligned to full length movies. In addition we also collected and aligned movie scripts used in prior work and compare the two sources of descriptions. We introduce the Large Scale Movie Description Challenge (LSMDC) which contains a parallel corpus of 128,118 sentences aligned to video clips from 200 movies (around 150 h of video in total). The goal of the challenge is to automatically generate descriptions for the movie clips. First we characterize the dataset by benchmarking different approaches for generating video descriptions. Comparing ADs to scripts, we find that ADs are more visual and describe precisely what is shown rather than what should happen according to the scripts created prior to movie production. Furthermore, we present and compare the results of several teams who participated in the challenges organized in the context of two workshops at ICCV 2015 and ECCV 2016.
2016-05-12
ArXiv (preprint)
doi.org
arxiv.org
Movie Description
Anna Rohrbach
Atousa Torabi
Marcus Rohrbach
Niket Tandon
Chris Pal
Hugo Larochelle
Aaron Courville
Bernt Schiele
2016-05-12
ArXiv (preprint)
doi.org
arxiv.org
Movie Description
Anna Rohrbach
Atousa Torabi
Marcus Rohrbach
Niket Tandon
Chris Pal
Hugo Larochelle
Aaron Courville
Bernt Schiele
Audio description (AD) provides linguistic descriptions of movies and allows visually impaired people to follow a movie along with their pee… (see more)rs. Such descriptions are by design mainly visual and thus naturally form an interesting data source for computer vision and computational linguistics. In this work we propose a novel dataset which contains transcribed ADs, which are temporally aligned to full length movies. In addition we also collected and aligned movie scripts used in prior work and compare the two sources of descriptions. We introduce the Large Scale Movie Description Challenge (LSMDC) which contains a parallel corpus of 128,118 sentences aligned to video clips from 200 movies (around 150 h of video in total). The goal of the challenge is to automatically generate descriptions for the movie clips. First we characterize the dataset by benchmarking different approaches for generating video descriptions. Comparing ADs to scripts, we find that ADs are more visual and describe precisely what is shown rather than what should happen according to the scripts created prior to movie production. Furthermore, we present and compare the results of several teams who participated in the challenges organized in the context of two workshops at ICCV 2015 and ECCV 2016.
2016-05-12
ArXiv (preprint)
doi.org
arxiv.org
Movie Description
Anna Rohrbach
Atousa Torabi
Marcus Rohrbach
Niket Tandon
Chris Pal
Hugo Larochelle
Aaron Courville
Bernt Schiele
2016-05-12
ArXiv (preprint)
doi.org
arxiv.org