Publications

Different scaling of linear models and deep learning in UKBiobank brain images versus machine-learning datasets
Marc-Andre Schulz
B.T. Thomas Yeo
Joshua T. Vogelstein
Janaina Mourao-Miranada
Jakob N. Kather
Konrad Paul Kording
Blake Richards
Danilo Bzdok
2020-08-25
Nature Communications (published)
doi.org
BIAS: Transparent reporting of biomedical image analysis challenges
Lena Maier-Hein
Annika Reinke
Michal Kozubek
Anne L. Martel
Tal Arbel
Matthias Eisenmann
Allan Hanbury
Pierre Jannin
Henning Müller
Sinan Onogur
Julio Saez-Rodriguez
Bram van Ginneken
Annette Kopp-Schneider
Bennett Landman
2020-08-21
Medical Image Analysis (published)
doi.org
arxiv.org
Laplacian Change Point Detection for Dynamic Graphs
Shenyang Huang
Yasmeen Hitti
Guillaume Rabusseau
Reihaneh Rabbany
2020-08-20
Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (published)
doi.org
arxiv.org
Adaptive Learning of Tensor Network Structures
Meraj Hashemizadeh
Michelle Liu
Jacob Miller
Guillaume Rabusseau
Tensor Networks (TN) offer a powerful framework to efficiently represent very high-dimensional objects. TN have recently shown their potenti… (see more)al for machine learning applications and offer a unifying view of common tensor decomposition models such as Tucker, tensor train (TT) and tensor ring (TR). However, identifying the best tensor network structure from data for a given task is challenging. In this work, we leverage the TN formalism to develop a generic and efficient adaptive algorithm to jointly learn the structure and the parameters of a TN from data. Our method is based on a simple greedy approach starting from a rank one tensor and successively identifying the most promising tensor network edges for small rank increments. Our algorithm can adaptively identify TN structures with small number of parameters that effectively optimize any differentiable objective function. Experiments on tensor decomposition, tensor completion and model compression tasks demonstrate the effectiveness of the proposed algorithm. In particular, our method outperforms the state-of-the-art evolutionary topology search [Li and Sun, 2020] for tensor decomposition of images (while being orders of magnitude faster) and finds efficient tensor network structures to compress neural networks outperforming popular TT based approaches [Novikov et al., 2015].
2020-08-12
ArXiv (preprint)
openreview.net
openreview.net
''COGITO in Space'': a thought experiment in exo-neurobiology
Daniela de Paulis
Stephen Whitmarsh
Robert Oostenveld
Guillaume Dumas
Michael Sanders
2020-08-04
(published)
doi.org
SeroTracker: a global SARS-CoV-2 seroprevalence dashboard
Rahul K. Arora
Abel Joseph
Jordan Van Wyk
Simona Rocco
Austin Atmaja
Ewan May
Tingting Yan
Niklas Bobrovitz
Jonathan Chevrier
Matthew P. Cheng
Tyler Williamson
David Buckeridge
2020-08-04
Lancet. Infectious Diseases (Print) (published)
doi.org
Implicit Regularization in Deep Learning: A View from Function Space
Aristide Baratin
Thomas George
César Laurent
(Rex) Devon Hjelm
Guillaume Lajoie
Pascal Vincent
Simon Lacoste-Julien
2020-08-03
ArXiv (preprint)
arxiv.org
arxiv.org
Implicit Regularization in Deep Learning: A View from Function Space
Aristide Baratin
Thomas George
César Laurent
(Rex) Devon Hjelm
Guillaume Lajoie
Pascal Vincent
Simon Lacoste-Julien
We approach the problem of implicit regularization in deep learning from a geometrical viewpoint. We highlight a possible regularization eff… (see more)ect induced by a dynamical alignment of the neural tangent features introduced by Jacot et al, along a small number of task-relevant directions. By extrapolating a new analysis of Rademacher complexity bounds in linear models, we propose and study a new heuristic complexity measure for neural networks which captures this phenomenon, in terms of sequences of tangent kernel classes along in the learning trajectories.
2020-08-03
ArXiv (preprint)
arxiv.org
arxiv.org
BDD-based optimization for the quadratic stable set problem
Jaime E. González
Andr'e Augusto Cire
Andrea Lodi
Louis-Martin Rousseau
2020-08-01
Discrete Optimization (published)
doi.org
BDD-based optimization for the quadratic stable set problem
Jaime E. González
Andr'e Augusto Cire
Andrea Lodi
Louis-Martin Rousseau
2020-08-01
Discrete Optimization (published)
doi.org
Optimal Local and Remote Controllers With Unreliable Uplink Channels: An Elementary Proof
Mohammad Afshari
Aditya Mahajan
Recently, a model of a decentralized control system with local and remote controllers connected over unreliable channels was presented in [… (see more)1]. The model has a nonclassical information structure that is not partially nested. Nonetheless, it is shown in [1] that the optimal control strategies are linear functions of the state estimate (which is a nonlinear function of the observations). Their proof is based on a fairly sophisticated dynamic programming argument. In this article, we present an alternative and elementary proof of the result which uses common information-based conditional independence and completion of squares.
2020-08-01
IEEE Transactions on Automatic Control (published)
doi.org
arxiv.org
Precision, Equity, and Public Health and Epidemiology Informatics – A Scoping Review
David Buckeridge
2020-08-01
Yearbook of Medical Informatics (published)
doi.org