activity
20182022
most citedTowards Trustworthy Automatic Diagnosis Systems by Emulating Doctors' Reasoning with Deep Reinforcement Learning

9 citations · 11 across the 2 of their papers we have counts for

collaborators

5 papers

cs.CL20229 cited

Towards Trustworthy Automatic Diagnosis Systems by Emulating Doctors' Reasoning with Deep Reinforcement Learning

Arsene Fansi Tchango, Rishab Goel, Julien Martel +3

The automation of the medical evidence acquisition and diagnosis process has recently attracted increasing attention in order to reduce the workload of doctors and democratize acce…

cs.CY20202 cited

COVI-AgentSim: an Agent-based Model for Evaluating Methods of Digital Contact Tracing

Prateek Gupta, Tegan Maharaj, Martin Weiss +26

The rapid global spread of COVID-19 has led to an unprecedented demand for effective methods to mitigate the spread of the disease, and various digital contact tracing (DCT) method…

cs.LG2020

Predicting Infectiousness for Proactive Contact Tracing

Yoshua Bengio, Prateek Gupta, Tegan Maharaj +20

The COVID-19 pandemic has spread rapidly worldwide, overwhelming manual contact tracing in many countries and resulting in widespread lockdowns for emergency containment. Large-sca…

physics.gen-ph2018

Combining neural networks and signed particles to simulate quantum systems more efficiently, Part III

Jean Michel Sellier, Gaetan Marceau Caron, Jacob Leygonie

This work belongs to a series of articles which have been dedicated to the combination of signed particles and neural networks to speed up the time-dependent simulation of quantum…

physics.comp-ph2018

Combining neural networks and signed particles to simulate quantum systems more efficiently, Part II

Jean Michel Sellier, Jacob Leygonie, Gaetan Marceau Caron

Recently the use of neural networks has been introduced in the context of the signed particle formulation of quantum mechanics to rapidly and reliably compute the Wigner kernel of…