Publications

Interpolated Adversarial Training: Achieving Robust Neural Networks Without Sacrificing Too Much Accuracy
Alex Lamb
Vikas Verma
Kenji Kawaguchi
Juho Kannala
Alexander Matyasko
Yoshua Bengio
Savya Khosla
Adversarial robustness has become a central goal in deep learning, both in theory and in practice. However, successful methods to improve th… (voir plus)e adversarial robustness (such as adversarial training) greatly hurt generalization performance on the unperturbed data. This could have a major impact on how achieving adversarial robustness affects real world systems (i.e. many may opt to forego robustness if it can improve accuracy on the unperturbed data). We propose Interpolated Adversarial Training, which employs recently proposed interpolation based training methods in the framework of adversarial training. On CIFAR-10, adversarial training increases the standard test error (when there is no adversary) from 4.43% to 12.32%, whereas with our Interpolated adversarial training we retain adversarial robustness while achieving a standard test error of only 6.45%. With our technique, the relative increase in the standard error for the robust model is reduced from 178.1% to just 45.5%.
2022-10-01
Neural Networks (publié)
doi.org
arxiv.org
Lifelong Topological Visual Navigation
Rey Reza Wiyatno
Anqi Xu
Liam Paull
Commonly, learning-based topological navigation approaches produce a local policy while preserving some loose connectivity of the space thro… (voir plus)ugh a topological map. Nevertheless, spurious or missing edges in the topological graph often lead to navigation failure. In this work, we propose a sampling-based graph building method, which results in sparser graphs yet with higher navigation performance compared to baseline methods. We also propose graph maintenance strategies that eliminate spurious edges and expand the graph as needed, which improves lifelong navigation performance. Unlike controllers that learn from fixed training environments, we show that our model can be fine-tuned using only a small number of collected trajectory images from a real-world environment where the agent is deployed. We demonstrate successful navigation after fine-tuning on real-world environments, and notably show significant navigation improvements over time by applying our lifelong graph maintenance strategies.
2022-10-01
IEEE Robotics and Automation Letters (publié)
doi.org
arxiv.org
MixEHR-Guided: A guided multi-modal topic modeling approach for large-scale automatic phenotyping using the electronic health record
Yuri Ahuja
Yuesong Zou
Aman Verma
David Buckeridge
Yue Li
2022-10-01
Journal of Biomedical Informatics (publié)
doi.org
Predicting histopathology markers of endometrial carcinoma with a quantitative image analysis approach based on spherical harmonics in multiparametric MRI.
Thierry L. Lefebvre
Ozan Ciga
Sahir Bhatnagar
Yoshiko Ueno
S. Saif
Eric Winter-Reinhold
Anthony Dohan
P. Soyer
Reza Forghani
Kaleem Siddiqi
Jan Seuntjens
Caroline Reinhold
Peter Savadjiev
2022-10-01
Diagnostic and Interventional Imaging (publié)
doi.org
Social isolation and the brain in the pandemic era
Danilo Bzdok
Robin I. M. Dunbar
2022-10-01
Nature Human Behaviour (publié)
doi.org
Synthetic data as an enabler for machine learning applications in medicine
Jean-Francois Rajotte
Robert Bergen
David Buckeridge
Khaled El Emam
Raymond Ng
Elissa Strome
2022-10-01
iScience (publié)
doi.org
The use of artificial intelligence and virtual reality in doctor-patient risk communication: A scoping review.
Ryan Antel
Samira Abbasgholizadeh-Rahimi
Elena Guadagno
Jason M. Harley
Dan Poenaru
2022-10-01
Patient Education and Counseling (publié)
doi.org
Evolution of cell size control is canalized towards adders or sizers by cell cycle structure and selective pressures
Felix Proulx-Giraldeau
Jan M Skotheim
Paul François
Cell size is controlled to be within a specific range to support physiological function. To control their size, cells use diverse mechanisms… (voir plus) ranging from ‘sizers’, in which differences in cell size are compensated for in a single cell division cycle, to ‘adders’, in which a constant amount of cell growth occurs in each cell cycle. This diversity raises the question why a particular cell would implement one rather than another mechanism? To address this question, we performed a series of simulations evolving cell size control networks. The size control mechanism that evolved was influenced by both cell cycle structure and specific selection pressures. Moreover, evolved networks recapitulated known size control properties of naturally occurring networks. If the mechanism is based on a G1 size control and an S/G2/M timer, as found for budding yeast and some human cells, adders likely evolve. But, if the G1 phase is significantly longer than the S/G2/M phase, as is often the case in mammalian cells in vivo, sizers become more likely. Sizers also evolve when the cell cycle structure is inverted so that G1 is a timer, while S/G2/M performs size control, as is the case for the fission yeast S. pombe. For some size control networks, cell size consistently decreases in each cycle until a burst of cell cycle inhibitor drives an extended G1 phase much like the cell division cycle of the green algae Chlamydomonas. That these size control networks evolved such self-organized criticality shows how the evolution of complex systems can drive the emergence of critical processes.
2022-09-30
eLife (publié)
doi.org
SPeCiaL: Self-Supervised Pretraining for Continual Learning
Lucas Caccia
Joelle Pineau
2022-09-28
Continual Semi-Supervised Learning (publié)
doi.org
arxiv.org
From analytic to synthetic-organizational pluralisms: A pluralistic enactive psychiatry
Christophe Gauld
Kristopher Nielsen
Manon Job
Hugo Bottemanne
Guillaume Dumas
2022-09-27
Frontiers in Psychiatry (publié)
doi.org
Recommendations and guidelines from the ISMRM Diffusion Study Group for preclinical diffusion MRI: Part 2 -- Ex vivo imaging
Kurt G Schilling
Francesco Grussu
Andrada Ianus
Brian Hansen
Manisha Aggarwal
Stijn Michielse
Fatima Nasrallah
Warda Syeda
Nian Wang
Jelle Veraart
Alard Roebroeck
Andrew F. Bagdasarian
Cornelius Eichner
Farshid Sepehrband
Jan Zimmermann
Ben Jeurissen
Lucio Frydman
Yohan van de Looij
David Hike
Jeff F. Dunn … (voir 30 de plus)
Karla Miller
Bennett Landman
Noam Shemesh
Arthur Anderson
Emilie McKinnon
Shawna Farquharson
Flavio Dell’Acqua
Carlo Pierpaoli
Ivana Drobnjak
Alexander Leemans
Kevin D. Harkins
Maxime Descoteaux
Duan Xu
Mathieu D. Santin
Samuel C. Grant
Andre Obenaus
Gene S. Kim
Dan Wu
Denis Le Bihan
Stephen J. Blackband
Luisa Ciobanu
Els Fieremans
Ruiliang Bai
Trygve B. Leergaard
Jiangyang Zhang
Tim B. Dyrby
G. Allan Johnson
Julien Cohen-Adad
Matthew D. Budde
Ileana O. Jelescu
2022-09-27
ArXiv (prépublication)
doi.org
arxiv.org
Estimating individual treatment effect on disability progression in multiple sclerosis using deep learning
Jean-Pierre R. Falet
Joshua D. Durso-Finley
Brennan Nichyporuk
Julien Schroeter
Francesca Bovis
Maria-Pia Sormani
Doina Precup
Tal Arbel
Douglas Arnold
2022-09-26
Nature Communications (publié)
doi.org