Publications

Multi-Task Self-Supervised Learning for Robust Speech Recognition
Mirco Ravanelli
Jianyuan Zhong
Santiago Pascual
Pawel Swietojanski
Joao Monteiro
Jan Trmal
Yoshua Bengio
Despite the growing interest in unsupervised learning, extracting meaningful knowledge from unlabelled audio remains an open challenge. To t… (voir plus)ake a step in this direction, we recently proposed a problem-agnostic speech encoder (PASE), that combines a convolutional encoder followed by multiple neural networks, called workers, tasked to solve self-supervised problems (i.e., ones that do not require manual annotations as ground truth). PASE was shown to capture relevant speech information, including speaker voice-print and phonemes. This paper proposes PASE+, an improved version of PASE for robust speech recognition in noisy and reverberant environments. To this end, we employ an online speech distortion module, that contaminates the input signals with a variety of random disturbances. We then propose a revised encoder that better learns short- and long-term speech dynamics with an efficient combination of recurrent and convolutional networks. Finally, we refine the set of workers used in self-supervision to encourage better cooperation.Results on TIMIT, DIRHA and CHiME-5 show that PASE+ significantly outperforms both the previous version of PASE as well as common acoustic features. Interestingly, PASE+ learns transferable representations suitable for highly mismatched acoustic conditions.
2020-05-04
ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (publié)
doi.org
arxiv.org
Continual Weight Updates and Convolutional Architectures for Equilibrium Propagation
Maxence Ernoult
Julie Grollier
Damien Querlioz
Yoshua Bengio
Benjamin Scellier
Equilibrium Propagation (EP) is a biologically inspired alternative algorithm to backpropagation (BP) for training neural networks. It appli… (voir plus)es to RNNs fed by a static input x that settle to a steady state, such as Hopfield networks. EP is similar to BP in that in the second phase of training, an error signal propagates backwards in the layers of the network, but contrary to BP, the learning rule of EP is spatially local. Nonetheless, EP suffers from two major limitations. On the one hand, due to its formulation in terms of real-time dynamics, EP entails long simulation times, which limits its applicability to practical tasks. On the other hand, the biological plausibility of EP is limited by the fact that its learning rule is not local in time: the synapse update is performed after the dynamics of the second phase have converged and requires information of the first phase that is no longer available physically. Our work addresses these two issues and aims at widening the spectrum of EP from standard machine learning models to more bio-realistic neural networks. First, we propose a discrete-time formulation of EP which enables to simplify equations, speed up training and extend EP to CNNs. Our CNN model achieves the best performance ever reported on MNIST with EP. Using the same discrete-time formulation, we introduce Continual Equilibrium Propagation (C-EP): the weights of the network are adjusted continually in the second phase of training using local information in space and time. We show that in the limit of slow changes of synaptic strengths and small nudging, C-EP is equivalent to BPTT (Theorem 1). We numerically demonstrate Theorem 1 and C-EP training on MNIST and generalize it to the bio-realistic situation of a neural network with asymmetric connections between neurons.
2020-04-29
ArXiv (prépublication)
arxiv.org
arxiv.org
Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims
Miles Brundage
Shahar Avin
Jasmine Wang
Haydn Belfield
Gretchen Krueger
Gillian K. Hadfield
Heidy Khlaaf
Jingying Yang
H. Toner
Ruth Catherine Fong
Tegan Maharaj
Pang Wei Koh
Sara Hooker
Jade Leung
Andrew John Trask
Emma Bluemke
Jonathan Lebensbold
Cullen C. O'keefe
Mark Koren
Th'eo Ryffel … (voir 39 de plus)
JB Rubinovitz
Tamay Besiroglu
Federica Carugati
Jack Clark
Peter Eckersley
Sarah de Haas
Maritza L. Johnson
Ben Laurie
Alex Ingerman
Igor Krawczuk
Amanda Askell
Rosario Cammarota
A. Lohn
David Scott Krueger
Charlotte Stix
Peter Mark Henderson
Logan Graham
Carina E. A. Prunkl
Bianca Martin
Elizabeth Seger
Noa Zilberman
Sean O hEigeartaigh
Frens Kroeger
Girish Sastry
R. Kagan
Adrian Weller
Brian Shek-kam Tse
Elizabeth Barnes
Allan Dafoe
Paul D. Scharre
Ariel Herbert-Voss
Martijn Rasser
Shagun Sodhani
Carrick Flynn
Thomas Krendl Gilbert
Lisa Dyer
Saif M. Khan
Yoshua Bengio
Markus Anderljung
2020-04-15
ArXiv (prépublication)
arxiv.org
arxiv.org
Establishing an evaluation metric to quantify climate change image realism
Sharon Zhou
Alexandra Luccioni
Gautier Cosne
Michael S. Bernstein
Yoshua Bengio
2020-04-07
Machine Learning: Science and Technology (publié)
doi.org
arxiv.org
Combating False Negatives in Adversarial Imitation Learning (Student Abstract)
Konrad Żołna
Chitwan Saharia
Léonard Boussioux
David Y. T. Hui
Maxime Chevalier-Boisvert
Dzmitry Bahdanau
Yoshua Bengio
2020-04-03
AAAI Conference on Artificial Intelligence (publié)
doi.org
Pruning for efficient hardware implementations of deep neural networks
Ghouthi Boukli Hacene
Vincent Gripon
Matthieu Arzel
Nicolas Farrugia
Yoshua Bengio
Continuous Domain Adaptation with Variational Domain-Agnostic Feature Replay
Qicheng Lao
Xiang Jiang
Mohammad Havaei
Yoshua Bengio
Learning in non-stationary environments is one of the biggest challenges in machine learning. Non-stationarity can be caused by either task … (voir plus)drift, i.e., the drift in the conditional distribution of labels given the input data, or the domain drift, i.e., the drift in the marginal distribution of the input data. This paper aims to tackle this challenge in the context of continuous domain adaptation, where the model is required to learn new tasks adapted to new domains in a non-stationary environment while maintaining previously learned knowledge. To deal with both drifts, we propose variational domain-agnostic feature replay, an approach that is composed of three components: an inference module that filters the input data into domain-agnostic representations, a generative module that facilitates knowledge transfer, and a solver module that applies the filtered and transferable knowledge to solve the queries. We address the two fundamental scenarios in continuous domain adaptation, demonstrating the effectiveness of our proposed approach for practical usage.
2020-03-09
ArXiv (prépublication)
arxiv.org
arxiv.org
On the Morality of Artificial Intelligence
Alexandra Luccioni
Yoshua Bengio
Examines ethical principles and guidelines that surround machine learning and artificial intelligence.
2020-03-01
IEEE Technology and Society Magazine (publié)
doi.org
arxiv.org
On Catastrophic Interference in Atari 2600 Games
William Fedus
Dibya Ghosh
John D. Martin
Marc Gendron-Bellemare
Yoshua Bengio
Hugo Larochelle
Model-free deep reinforcement learning is sample inefficient. One hypothesis -- speculated, but not confirmed -- is that catastrophic interf… (voir plus)erence within an environment inhibits learning. We test this hypothesis through a large-scale empirical study in the Arcade Learning Environment (ALE) and, indeed, find supporting evidence. We show that interference causes performance to plateau; the network cannot train on segments beyond the plateau without degrading the policy used to reach there. By synthetically controlling for interference, we demonstrate performance boosts across architectures, learning algorithms and environments. A more refined analysis shows that learning one segment of a game often increases prediction errors elsewhere. Our study provides a clear empirical link between catastrophic interference and sample efficiency in reinforcement learning.
2020-02-28
ArXiv (prépublication)
arxiv.org
arxiv.org
Neural Bayes: A Generic Parameterization Method for Unsupervised Representation Learning
Devansh Arpit
Huan Wang
Caiming Xiong
Richard Socher
Yoshua Bengio
2020-02-20
ArXiv (prépublication)
arxiv.org
arxiv.org
HighRes-net: Recursive Fusion for Multi-Frame Super-Resolution of Satellite Imagery
Michel Deudon
Alfredo Kalaitzis
Israel Goytom
Md Rifat Arefin
Zhichao Lin
Kris Sankaran
Vincent Michalski
Samira Ebrahimi Kahou
Julien Cornebise
Yoshua Bengio
Generative deep learning has sparked a new wave of Super-Resolution (SR) algorithms that enhance single images with impressive aesthetic res… (voir plus)ults, albeit with imaginary details. Multi-frame Super-Resolution (MFSR) offers a more grounded approach to the ill-posed problem, by conditioning on multiple low-resolution views. This is important for satellite monitoring of human impact on the planet -- from deforestation, to human rights violations -- that depend on reliable imagery. To this end, we present HighRes-net, the first deep learning approach to MFSR that learns its sub-tasks in an end-to-end fashion: (i) co-registration, (ii) fusion, (iii) up-sampling, and (iv) registration-at-the-loss. Co-registration of low-resolution views is learned implicitly through a reference-frame channel, with no explicit registration mechanism. We learn a global fusion operator that is applied recursively on an arbitrary number of low-resolution pairs. We introduce a registered loss, by learning to align the SR output to a ground-truth through ShiftNet. We show that by learning deep representations of multiple views, we can super-resolve low-resolution signals and enhance Earth Observation data at scale. Our approach recently topped the European Space Agency's MFSR competition on real-world satellite imagery.
2020-02-15
ArXiv (prépublication)
arxiv.org
arxiv.org
Modeling Cloud Reflectance Fields using Conditional Generative Adversarial Networks
Victor Schmidt
Mustafa Alghali
Kris Sankaran
Tianle Yuan
Yoshua Bengio
We introduce a conditional Generative Adversarial Network (cGAN) approach to generate cloud reflectance fields (CRFs) conditioned on large s… (voir plus)cale meteorological variables such as sea surface temperature and relative humidity. We show that our trained model can generate realistic CRFs from the corresponding meteorological observations, which represents a step towards a data-driven framework for stochastic cloud parameterization.
2020-02-10
ArXiv (prépublication)
arxiv.org
arxiv.org