Publications

Modelling Latent Translations for Cross-Lingual Transfer
Edoardo Ponti
Julia Kreutzer
Ivan Vulić
Siva Reddy
2021-07-23
ArXiv (prépublication)
arxiv.org
arxiv.org
Automatic multiclass intramedullary spinal cord tumor segmentation on MRI with deep learning
Andreanne Lemay
Charley Gros
Zhizheng Zhuo
Jie Zhang
Yunyun Duan
Julien Cohen-Adad
Yaou Liu
2021-07-22
NeuroImage : Clinical (publié)
doi.org
Exploration-Driven Representation Learning in Reinforcement Learning
Akram Erraqabi
Harry Zhao
Mingde Zhao
Marlos C. Machado
Yoshua Bengio
Sainbayar Sukhbaatar
Ludovic Denoyer
Alessandro Lazaric
Learning reward-agnostic representations is an emerging paradigm in reinforcement learning. These representations can be leveraged for sever… (voir plus)al purposes ranging from reward shaping to skill discovery. Nevertheless, in order to learn such representations, existing methods often rely on assuming uniform access to the state space. With such a privilege, the agent’s coverage of the environment can be limited which hurts the quality of the learned representations. In this work, we introduce a method that explicitly couples representation learning with exploration when the agent is not provided with a uniform prior over the state space. Our method learns representations that constantly drive exploration while the data generated by the agent’s exploratory behavior drives the learning of better representations. We empirically validate our approach in goal-achieving tasks, demonstrating that the learned representation captures the dynamics of the environment, leads to more accurate value estimation, and to faster credit assignment, both when used for control and for reward shaping. Finally, the exploratory policy that emerges from our approach proves to be successful at continuous navigation tasks with sparse rewards.
2021-07-22
ICML.cc/2021/Workshop/URL (poster)
openreview.net
openreview.net
Diffusion magnetic resonance imaging reveals tract‐specific microstructural correlates of electrophysiological impairments in non‐myelopathic and myelopathic spinal cord compression
Jan Valosek
René Labounek
Tomáš Horák
Magda Horáková
Petr Bednařík
Miloš Keřkovský
Jan Kočica
Tomáš Rohan
Christophe Lenglet
Julien Cohen-Adad
Petr Hluštík
Eva Vlčková
Zdeněk Kadaňka
Josef Bednařík
Alena Svatkova
Non‐myelopathic degenerative cervical spinal cord compression (NMDC) frequently occurs throughout aging and may progress to potentially ir… (voir plus)reversible degenerative cervical myelopathy (DCM). Whereas standard clinical magnetic resonance imaging (MRI) and electrophysiological measures assess compression severity and neurological dysfunction, respectively, underlying microstructural deficits still have to be established in NMDC and DCM patients. The study aims to establish tract‐specific diffusion MRI markers of electrophysiological deficits to predict the progression of asymptomatic NMDC to symptomatic DCM.
2021-07-21
European Journal of Neurology (publié)
doi.org
Dynamic shimming in the cervical spinal cord for multi-echo gradient-echo imaging at 3 T
Eva Alonso‐Ortiz
Daniel Papp
Alain D’astous
Julien Cohen-Adad
2021-07-21
ArXiv (preprint)
doi.org
arxiv.org
Parametric Scattering Networks
Shanel Gauthier
Benjamin Th'erien
Laurent Alséne-Racicot
Irina Rish
Eugene Belilovsky
Michael Eickenberg
Guy Wolf
The wavelet scattering transform creates geometric in-variants and deformation stability. In multiple signal do-mains, it has been shown to … (voir plus)yield more discriminative rep-resentations compared to other non-learned representations and to outperform learned representations in certain tasks, particularly on limited labeled data and highly structured signals. The wavelet filters used in the scattering trans-form are typically selected to create a tight frame via a pa-rameterized mother wavelet. In this work, we investigate whether this standard wavelet filterbank construction is op-timal. Focusing on Morlet wavelets, we propose to learn the scales, orientations, and aspect ratios of the filters to produce problem-specific parameterizations of the scattering transform. We show that our learned versions of the scattering transform yield significant performance gains in small-sample classification settings over the standard scat-tering transform. Moreover, our empirical results suggest that traditional filterbank constructions may not always be necessary for scattering transforms to extract effective rep-resentations.
2021-07-20
ArXiv (preprint)
doi.org
arxiv.org
Combating False Negatives in Adversarial Imitation Learning
Konrad Żołna
Chitwan Saharia
Léonard Boussioux
David Y. T. Hui
Maxime Chevalier-Boisvert
Dzmitry Bahdanau
Yoshua Bengio
In adversarial imitation learning, a discriminator is trained to differentiate agent episodes from expert demonstrations representing the de… (voir plus)sired behavior. However, as the trained policy learns to be more successful, the negative examples (the ones produced by the agent) become increasingly similar to expert ones. Despite the fact that the task is successfully accomplished in some of the agent's trajectories, the discriminator is trained to output low values for them. We hypothesize that this inconsistent training signal for the discriminator can impede its learning, and consequently leads to worse overall performance of the agent. We show experimental evidence for this hypothesis and that the ‘False Negatives’ (i.e. successful agent episodes) significantly hinder adversarial imitation learning, which is the first contribution of this paper. Then, we propose a method to alleviate the impact of false negatives and test it on the BabyAI environment. This method consistently improves sample efficiency over the baselines by at least an order of magnitude.
2021-07-18
2021 International Joint Conference on Neural Networks (IJCNN) (publié)
doi.org
arxiv.org
VirtualGAN: Reducing Mode Collapse in Generative Adversarial Networks Using Virtual Mapping
Adel Abusitta
Omar Abdel Wahab
Benjamin Fung
This paper introduces a new framework for reducing mode collapse in Generative adversarial networks (GANs). The problem occurs when the gene… (voir plus)rator learns to map several various input values (z) to the same output value, which makes the generator fail to capture all modes of the true data distribution. As a result, the diversity of synthetically produced data is lower than that of the real data. To address this problem, we propose a new and simple framework for training GANs based on the concept of virtual mapping. Our framework integrates two processes into GANs: merge and split. The merge process merges multiple data points (samples) into one before training the discriminator. In this way, the generator would be trained to capture the merged-data distribution rather than the (unmerged) data distribution. After the training, the split process is applied to the generator's output in order to split its contents and produce diverse modes. The proposed framework increases the chance of capturing diverse modes through enabling an indirect or virtual mapping between an input z value and multiple data points. This, in turn, enhances the chance of generating more diverse modes. Our results show the effectiveness of our framework compared to the existing approaches in terms of reducing the mode collapse problem.
2021-07-18
IEEE International Joint Conference on Neural Network (publié)
doi.org
Symptom network analysis of the sleep disorders diagnostic criteria based on the clinical text of the ICSD‐3
Christophe Gauld
Régis Lopez
C. Morin
Pierre A. GEOFFROY
Julien Maquet
Pierre Desvergnes
Aileen McGonigal
Yves A. Dauvilliers
Pierre Philip
Guillaume Dumas
J-a Micoulaud-franchi
The third edition of the International Classification of Sleep Disorders (ICSD‐3) is the authoritative clinical text for the diagnosis of … (voir plus)sleep disorders. An important issue of sleep nosology is to better understand the relationship between symptoms found in conventional diagnostic manuals and to compare classifications. Nevertheless, to our knowledge, there is no specific exhaustive work on the general structure of the networks of symptoms of sleep disorders as described in diagnostic manuals. The general aim of the present study was to use symptom network analysis to explore the diagnostic criteria in the ICSD‐3 manual. The ICSD‐3 diagnostic criteria related to clinical manifestations were systematically identified, and the units of analysis (symptoms) were labelled from these clinical manifestation diagnostic criteria using three rules (“Conservation”, “Splitting”, “Lumping”). A total of 37 of the 43 main sleep disorders with 160 units of analysis from 114 clinical manifestations in the ICSD‐3 were analysed. A symptom network representing all individual ICSD‐3 criteria and connections between them was constructed graphically (network estimation), quantified with classical metrics (network inference with global and local measures) and tested for robustness. The global measure of the sleep symptoms network shows that it can be considered as a small world, suggesting a strong interconnection between symptoms in the ICSD‐3. Local measures show the central role of three kinds of bridge sleep symptoms: daytime sleepiness, insomnia, and behaviour during sleep symptoms. Such a symptom network analysis of the ICSD‐3 structure could provide a framework for better systematising and organising symptomatology in sleep medicine.
2021-07-16
Journal of Sleep Research (published)
doi.org
Symptom network analysis of the sleep disorders diagnostic criteria based on the clinical text of the ICSD‐3
Christophe Gauld
Régis Lopez
Charles Morin
Pierre A. GEOFFROY
Julien Maquet
Pierre Desvergnes
Aileen McGonigal
Yves Dauvilliers
Pierre Philip
Guillaume Dumas
Jean‐Arthur Micoulaud‐Franchi
2021-07-16
Journal of Sleep Research (publié)
doi.org
Trips and neurotransmitters: Discovering principled patterns across 6850 hallucinogenic experiences
Galen Ballentine
Samuel Freesun Friedman
Danilo Bzdok
2021-07-14
bioRxiv (prépublication)
doi.org
Design of Hesitation Gestures for Nonverbal Human-Robot Negotiation of Conflicts
AJung Moon
Maneezhay Hashmi
H. F. Machiel Van Der Loos
Elizabeth A. Croft
Aude Billard
When the question of who should get access to a communal resource first is uncertain, people often negotiate via nonverbal communication to … (voir plus)resolve the conflict. What should a robot be programmed to do when such conflicts arise in Human-Robot Interaction? The answer to this question varies depending on the context of the situation. Learning from how humans use hesitation gestures to negotiate a solution in such conflict situations, we present a human-inspired design of nonverbal hesitation gestures that can be used for Human-Robot Negotiation. We extracted characteristic features of such negotiative hesitations humans use, and subsequently designed a trajectory generator (Negotiative Hesitation Generator) that can re-create the features in robot responses to conflicts. Our human-subjects experiment demonstrates the efficacy of the designed robot behaviour against non-negotiative stopping behaviour of a robot. With positive results from our human-robot interaction experiment, we provide a validated trajectory generator with which one can explore the dynamics of human-robot nonverbal negotiation of resource conflicts.
2021-07-11
ACM Transactions on Human-Robot Interaction (publié)
doi.org