Publications

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
Finding the needle in a high-dimensional haystack: Canonical correlation analysis for neuroscientists
Hao-Ting Wang
Jonathan Smallwood
Janaina Mourão Miranda
Cedric Huchuan Xia
Theodore D. Satterthwaite
Danielle S. Bassett
Danilo Bzdok
2020-08-01
NeuroImage (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
Renewal Monte Carlo: Renewal Theory-Based Reinforcement Learning
Jayakumar Subramanian
Aditya Mahajan
An online reinforcement learning algorithm called renewal Monte Carlo (RMC) is presented. RMC works for infinite horizon Markov decision pro… (see more)cesses with a designated start state. RMC is a Monte Carlo algorithm that retains the key advantages of Monte Carlo—viz., simplicity, ease of implementation, and low bias—while circumventing the main drawbacks of Monte Carlo—viz., high variance and delayed updates. Given a parameterized policy
2020-08-01
IEEE Transactions on Automatic Control (published)
doi.org
Inferring disease subtypes from clusters in explanation space
Marc-Andre Schulz
Matt Chapman-Rounds
Manisha Verma
Danilo Bzdok
Konstantinos Georgatzis
2020-07-30
Scientific Reports (published)
doi.org
Deriving Differential Target Propagation from Iterating Approximate Inverses
Yoshua Bengio
2020-07-29
ArXiv (preprint)
arxiv.org
arxiv.org
Predicting COVID-19 Pneumonia Severity on Chest X-ray With Deep Learning
Joseph Paul Cohen
Lan Dao
Paul Morrison
Karsten Roth
Yoshua Bengio
Beiyi Shen
Almas F Abbasi
Hoshmand Kochi Mahsa
Marzyeh Ghassemi
Haifang Li
Tim Q Duong
Introduction The need to streamline patient management for coronavirus disease-19 (COVID-19) has become more pressing than ever. Chest X-ray… (see more)s (CXRs) provide a non-invasive (potentially bedside) tool to monitor the progression of the disease. In this study, we present a severity score prediction model for COVID-19 pneumonia for frontal chest X-ray images. Such a tool can gauge the severity of COVID-19 lung infections (and pneumonia in general) that can be used for escalation or de-escalation of care as well as monitoring treatment efficacy, especially in the ICU. Methods Images from a public COVID-19 database were scored retrospectively by three blinded experts in terms of the extent of lung involvement as well as the degree of opacity. A neural network model that was pre-trained on large (non-COVID-19) chest X-ray datasets is used to construct features for COVID-19 images which are predictive for our task. Results This study finds that training a regression model on a subset of the outputs from this pre-trained chest X-ray model predicts our geographic extent score (range 0-8) with 1.14 mean absolute error (MAE) and our lung opacity score (range 0-6) with 0.78 MAE. Conclusions These results indicate that our model’s ability to gauge the severity of COVID-19 lung infections could be used for escalation or de-escalation of care as well as monitoring treatment efficacy, especially in the ICU. To enable follow up work, we make our code, labels, and data available online.
2020-07-28
Cureus (published)
doi.org
arxiv.org