Publications

Bridging the Gap Between Adversarial Robustness and Optimization Bias
Fartash Faghri
Cristina Vasconcelos
David J Fleet
Fabian Pedregosa
Nicolas Roux
2021-02-16
ArXiv (prépublication)
arxiv.org
arxiv.org
A Strategic Markovian Traffic Equilibrium Model for Capacitated Networks
Maëlle Zimmermann
Emma Frejinger
Patrice Marcotte
In the realm of traffic assignment over a network involving rigid arc capacities, the aim of the present work is to generalize the model of … (voir plus)Marcotte, Nguyen, and Schoeb [Marcotte P, Nguyen S, Schoeb A (2004) A strategic flow model of traffic assignment in static capacitated networks. Oper. Res. 52(2):191–212.] by casting it within a stochastic user equilibrium framework. The strength of the proposed model is to incorporate two sources of stochasticity stemming, respectively, from the users’ imperfect knowledge regarding arc costs (represented by a discrete choice model) and the probability of not accessing saturated arcs. Moreover, the arc-based formulation extends the Markovian traffic equilibrium model of Baillon and Cominetti [Baillon JB, Cominetti R ( 2008 ) Markovian traffic equilibrium. Math. Programming 111(1-2):33–56.] through the explicit consideration of capacities. This paper is restricted to the case of acyclic networks, for which we present solution algorithms and numerical experiments.
2021-02-16
Transportation Science (publié)
doi.org
Analyzing the Contribution of Ethical Charters to Building the Future of Artificial Intelligence Governance
Lyse Langlois
Catherine Régis
2021-02-06
Reflections on Artificial Intelligence for Humanity (publié)
doi.org
Structured Sparsity Inducing Adaptive Optimizers for Deep Learning
Tristan Deleu
Yoshua Bengio
The parameters of a neural network are naturally organized in groups, some of which might not contribute to its overall performance. To prun… (voir plus)e out unimportant groups of parameters, we can include some non-differentiable penalty to the objective function, and minimize it using proximal gradient methods. In this paper, we derive the weighted proximal operator, which is a necessary component of these proximal methods, of two structured sparsity inducing penalties. Moreover, they can be approximated efficiently with a numerical solver, and despite this approximation, we prove that existing convergence guarantees are preserved when these operators are integrated as part of a generic adaptive proximal method. Finally, we show that this adaptive method, together with the weighted proximal operators derived here, is indeed capable of finding solutions with structure in their sparsity patterns, on representative examples from computer vision and natural language processing.
2021-02-06
ArXiv (prépublication)
arxiv.org
arxiv.org
Enabling Secure Trustworthiness Assessment and Privacy Protection in Integrating Data for Trading Person-Specific Information
Rashid Hussain Khokhar
Farkhund Iqbal
Benjamin C. M. Fung
Jamal Bentahar
With increasing adoption of cloud services in the e-market, collaboration between stakeholders is easier than ever. Consumer stakeholders de… (voir plus)mand data from various sources to analyze trends and improve customer services. Data-as-a-service enables data integration to serve the demands of data consumers. However, the data must be of good quality and trustful for accurate analysis and effective decision making. In addition, a data custodian or provider must conform to privacy policies to avoid potential penalties for privacy breaches. To address these challenges, we propose a twofold solution: 1) we present the first information entropy-based trust computation algorithm, IEB_Trust, that allows a semitrusted arbitrator to detect the covert behavior of a dishonest data provider and chooses the qualified providers for a data mashup and 2) we incorporate the Vickrey–Clarke–Groves (VCG) auction mechanism for the valuation of data providers’ attributes into the data mashup process. Experiments on real-life data demonstrate the robustness of our approach in restricting dishonest providers from participation in the data mashup and improving the efficiency in comparison to provenance-based approaches. Furthermore, we derive the monetary shares for the chosen providers from their information utility and trust scores over the differentially private release of the integrated dataset under their joint privacy requirements.
2021-01-31
IEEE transactions on engineering management (publié)
doi.org
The neural correlates of ongoing conscious thought
Jonathan Smallwood
Adam Turnbull
Hao-Ting Wang
Nerissa S.P. Ho
Giulia L. Poerio
Theodoros Karapanagiotidis
Delali Konu
Brontë Mckeown
Meichao Zhang
Charlotte Murphy
Deniz Vatansever
Danilo Bzdok
Mahiko Konishi
Robert Leech
Paul Seli
Jonathan W. Schooler
Boris C Bernhardt
Daniel S. Margulies
Elizabeth Jefferies
2021-01-31
iScience (publié)
doi.org
Training neural networks to recognize speech increased their correspondence to the human auditory pathway but did not yield a shared hierarchy of acoustic features
Jessica A.F. Thompson
Yoshua Bengio
Elia Formisano
Marc Schönwiesner
Trained CNNs more similar to auditory fMRI activity than untrainedNo evidence of a shared representational hierarchy for acoustic featuresAl… (voir plus)l ROIs were most similar to the first fully-connected layerCNN performance on speech recognition task positively associated with fmri similarity
2021-01-26
bioRxiv (prépublication)
doi.org
A systematic analysis of ICSD-3 diagnostic criteria and proposal for further structured iteration.
Christophe Gauld
Régis Lopez
Pierre A. GEOFFROY
Charles Morin
Kelly Guichard
Elodie Giroux
Yves Dauvilliers
Guillaume Dumas
Pierre Philip
Jean‐Arthur Micoulaud‐Franchi
2021-01-25
Sleep Medicine Reviews (publié)
doi.org
Learning the Latent Space of Robot Dynamics for Cutting Interaction Inference
Sahand Rezaei-Shoshtari
David Meger
Inna Sharf
Utilization of latent space to capture a lower-dimensional representation of a complex dynamics model is explored in this work. The targeted… (voir plus) application is of a robotic manipulator executing a complex environment interaction task, in particular, cutting a wooden object. We train two flavours of Variational Autoencoders---standard and Vector-Quantised---to learn the latent space which is then used to infer certain properties of the cutting operation, such as whether the robot is cutting or not, as well as, material and geometry of the object being cut. The two VAE models are evaluated with reconstruction, prediction and a combined reconstruction/prediction decoders. The results demonstrate the expressiveness of the latent space for robotic interaction inference and the competitive prediction performance against recurrent neural networks.
2021-01-23
2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (publié)
doi.org
arxiv.org
Author response: Functional specialization within the inferior parietal lobes across cognitive domains
Ole Numssen
Danilo Bzdok
Gesa Hartwigsen
2021-01-20
(publié)
doi.org
Correction to: The patient advisor, an organizational resource as a lever for an enhanced oncology patient experience (PAROLEonco): a longitudinal multiple case study protocol
M. P. Pomey
M. de Guise
M. Desforges
K. Bouchard
C. Vialaron
L. Normandin
M. Iliescu-Nelea
I. Fortin
I. Ganache
C. Régis
Z. Rosberger
D. Charpentier
L. Bélanger
M. Dorval
D. P. Ghadiri
M. Lavoie-Tremblay
A. Boivin
J. F. Pelletier
N. Fernandez
A. M. Danino
An amendment to this paper has been published and can be accessed via the original article.
2021-01-13
BMC Health Services Research (publié)
doi.org
Deep learning identifies partially overlapping subnetworks in the human social brain
Hannah Kiesow
R. Nathan Spreng
Avram J. Holmes
Mallar Chakravarty
Andre Marquand
B.T. Thomas Yeo
Danilo Bzdok
2021-01-13
Communications Biology (publié)
doi.org