Publications

Learning to live with Dale's principle: ANNs with separate excitatory and inhibitory units
Jonathan Cornford
Damjan Kalajdzievski
Marco Leite
Amélie Lamarquette
Dimitri Michael Kullmann
Blake Richards
The units in artificial neural networks (ANNs) can be thought of as abstractions of biological neurons, and ANNs are increasingly used in ne… (see more)uroscience research. However, there are many important differences between ANN units and real neurons. One of the most notable is the absence of Dale's principle, which ensures that biological neurons are either exclusively excitatory or inhibitory. Dale's principle is typically left out of ANNs because its inclusion impairs learning. This is problematic, because one of the great advantages of ANNs for neuroscience research is their ability to learn complicated, realistic tasks. Here, by taking inspiration from feedforward inhibitory interneurons in the brain we show that we can develop ANNs with separate populations of excitatory and inhibitory units that learn just as well as standard ANNs. We call these networks Dale's ANNs (DANNs). We present two insights that enable DANNs to learn well: (1) DANNs are related to normalization schemes, and can be initialized such that the inhibition centres and standardizes the excitatory activity, (2) updates to inhibitory neuron parameters should be scaled using corrections based on the Fisher Information matrix. These results demonstrate how ANNs that respect Dale's principle can be built without sacrificing learning performance, which is important for future work using ANNs as models of the brain. The results may also have interesting implications for how inhibitory plasticity in the real brain operates.
2021-01-12
ICLR.cc/2021/Conference (poster)
doi.org
openreview.net
Predicting Infectiousness for Proactive Contact Tracing
Yoshua Bengio
Prateek Gupta
Tegan Maharaj
Nasim Rahaman
Martin Weiss
Tristan Deleu
Eilif Benjamin Muller
Meng Qu
Victor Schmidt
Pierre-Luc St-Charles
Hannah Alsdurf
Olexa Bilaniuk
David Buckeridge
gaetan caron
pierre luc carrier
Joumana Ghosn
satya ortiz gagne
Chris Pal
Irina Rish
Bernhard Schölkopf … (see 3 more)
abhinav sharma
Jian Tang
andrew williams
The COVID-19 pandemic has spread rapidly worldwide, overwhelming manual contact tracing in many countries and resulting in widespread lockdo… (see more)wns for emergency containment. Large-scale digital contact tracing (DCT) has emerged as a potential solution to resume economic and social activity while minimizing spread of the virus. Various DCT methods have been proposed, each making trade-offs between privacy, mobility restrictions, and public health. The most common approach, binary contact tracing (BCT), models infection as a binary event, informed only by an individual's test results, with corresponding binary recommendations that either all or none of the individual's contacts quarantine. BCT ignores the inherent uncertainty in contacts and the infection process, which could be used to tailor messaging to high-risk individuals, and prompt proactive testing or earlier warnings. It also does not make use of observations such as symptoms or pre-existing medical conditions, which could be used to make more accurate infectiousness predictions. In this paper, we use a recently-proposed COVID-19 epidemiological simulator to develop and test methods that can be deployed to a smartphone to locally and proactively predict an individual's infectiousness (risk of infecting others) based on their contact history and other information, while respecting strong privacy constraints. Predictions are used to provide personalized recommendations to the individual via an app, as well as to send anonymized messages to the individual's contacts, who use this information to better predict their own infectiousness, an approach we call proactive contact tracing (PCT). We find a deep-learning based PCT method which improves over BCT for equivalent average mobility, suggesting PCT could help in safe re-opening and second-wave prevention.
2021-01-12
ICLR.cc/2021/Conference (spotlight)
openreview.net
openreview.net
The patient advisor, an organizational resource as a lever for an enhanced oncology patient experience (PAROLE-onco): a longitudinal multiple case study protocol
Marie-Pascale Pomey
Michèle de Guise
Mado Desforges
Karine Bouchard
Cécile Vialaron
Louise Normandin
Monica Iliescu‐Nelea
Israël Fortin
Isabelle Ganache
Catherine Régis
Zeev Rosberger
Danielle Charpentier
L. Bélanger
Michel Dorval
Djahanchah Philip Ghadiri
Mélanie Lavoie-Tremblay
A. Boivin
Jean-François Pelletier
Nicolas Fernandez
Alain M. Danino
2021-01-04
BMC Health Services Research (published)
doi.org
Abg-CoQA: Clarifying Ambiguity in Conversational Question Answering
Meiqi Guo
Mingda Zhang
Siva Reddy
Malihe Alikhani
Effective communication is about the dissemination of properly worded meaningful ideas/messages that are comprehensible to both sen… (see more)der and receiver and which ultimately can attract the desired response or feedback. For machines to engage in a conversation, it is therefore essential to enable them to clarify ambiguity and achieve a common ground. We introduce Abg-CoQA, a novel dataset for clarifying ambiguity in Conversational Question Answering systems. Our dataset contains 9k questions with answers where 1k questions are ambiguous, obtained from 4k text passages from five diverse domains. For ambiguous questions, a clarification conversational turn is collected. We evaluate strong language generation models and conversational question answering models on Abg-CoQA. The best-performing system achieves a BLEU-1 score of 12.9% on generating clarification question, which is 27.9 points behind human performance (40.8%); and a F1 score of 40.1% on question answering after clarification, which is 35.1 points behind human performance (75.2%), indicating there is ample room for improvement.
2021-01-01
Conference on Automated Knowledge Base Construction (published)
doi.org
openreview.net
Accounting for Variance in Machine Learning Benchmarks
Xavier Bouthillier
Pierre Delaunay
Mirko Bronzi
Assya Trofimov
Brennan Nichyporuk
Justin Szeto
Naz Sepah
Edward Raff
Kanika Madan
Vikram Voleti
Samira Ebrahimi Kahou
Vincent Michalski
Dmitriy Serdyuk
Tal Arbel
Chris Pal
Gael Varoquaux
Pascal Vincent
Strong empirical evidence that one machine-learning algorithm A outperforms another one B ideally calls for multiple trials optimizing the l… (see more)earning pipeline over sources of variation such as data sampling, data augmentation, parameter initialization, and hyperparameters choices. This is prohibitively expensive, and corners are cut to reach conclusions. We model the whole benchmarking process, revealing that variance due to data sampling, parameter initialization and hyperparameter choice impact markedly the results. We analyze the predominant comparison methods used today in the light of this variance. We show a counter-intuitive result that adding more sources of variation to an imperfect estimator approaches better the ideal estimator at a 51 times reduction in compute cost. Building on these results, we study the error rate of detecting improvements, on five different deep-learning tasks/architectures. This study leads us to propose recommendations for performance comparisons.
2021-01-01
MLSys (published)
arxiv.org
arxiv.org
Active Learning for Capturing Human Decision Policies in a Data Frugal Context
Loïc Grossetête
Alexandre Marois
Bénédicte Chatelais
Christian Gagné
Daniel Lafond
2021-01-01
International Conference on Machine Learning, Optimization, and Data Science (published)
doi.org
ADEPT: An Adjective-Dependent Plausibility Task
Ali Emami
Ian Porada
Alexandra Olteanu
Kaheer Suleman
Adam Trischler
Jackie Cheung
2021-01-01
Annual Meeting of the Association for Computational Linguistics (published)
doi.org
Adversarial Feature Desensitization
Pouya Bashivan
Reza Bayat
Adam Ibrahim
Kartik Ahuja
Mojtaba Faramarzi
Touraj Laleh
Blake Richards
Irina Rish
Neural networks are known to be vulnerable to adversarial attacks -- slight but carefully constructed perturbations of the inputs which can … (see more)drastically impair the network's performance. Many defense methods have been proposed for improving robustness of deep networks by training them on adversarially perturbed inputs. However, these models often remain vulnerable to new types of attacks not seen during training, and even to slightly stronger versions of previously seen attacks. In this work, we propose a novel approach to adversarial robustness, which builds upon the insights from the domain adaptation field. Our method, called Adversarial Feature Desensitization (AFD), aims at learning features that are invariant towards adversarial perturbations of the inputs. This is achieved through a game where we learn features that are both predictive and robust (insensitive to adversarial attacks), i.e. cannot be used to discriminate between natural and adversarial data. Empirical results on several benchmarks demonstrate the effectiveness of the proposed approach against a wide range of attack types and attack strengths. Our code is available at https://github.com/BashivanLab/afd.
openreview.net
An Analysis of the Adaptation Speed of Causal Models
Rémi LE PRIOL
Reza Babanezhad Harikandeh
Yoshua Bengio
Simon Lacoste-Julien
2021-01-01
AISTATS (published)
proceedings.mlr.press
arxiv.org
Analyzing the Contribution of Ethical Charters to Building the Future of Artificial Intelligence Governance
Lyse Langlois
Catherine Régis
2021-01-01
Reflections on Artificial Intelligence for Humanity (published)
doi.org
Batch Reinforcement Learning Through Continuation Method
Yijie Guo
Shengyu Feng
Nicolas Le Roux
Ed Chi
Honglak Lee
Minmin Chen
Many real-world applications of reinforcement learning (RL) require the agent to learn from a fixed set of trajectories, without collecting … (see more)new interactions. Policy optimization under this setting is extremely challenging as: 1) the geometry of the objective function is hard to optimize efficiently; 2) the shift of data distributions causes high noise in the value estimation. In this work, we propose a simple yet effective policy iteration approach to batch RL using global optimization techniques known as continuation. By constraining the difference between the learned policy and the behavior policy that generates the fixed trajectories, and continuously relaxing the constraint, our method 1) helps the agent escape local optima; 2) reduces the error in policy evaluation in the optimization procedure. We present results on a variety of control tasks, game environments, and a recommendation task to empirically demonstrate the efficacy of our proposed method.
2021-01-01
ICLR (published)
openreview.net
openreview.net
Can Open Source Licenses Help Regulate Lethal Autonomous Weapons?
Cheng Lin
AJung Moon
Lethal autonomous weapon systems (LAWS, ethal autonomous weapon also known as killer robots) are a real and emerging technology that have th… (see more)e potential to radically transform warfare. Because of the myriad of moral, legal, privacy, and security risks the technology introduces, many scholars and advocates have called for a ban on the development, production, and use of fully autonomous weapons [1], [2].
2021-01-01
IEEE technology & society magazine (published)
doi.org