Publications

COVI White Paper - Version 1.1
Hannah Alsdurf
Edmond Belliveau
Yoshua Bengio
Tristan Deleu
Prateek Gupta
Daphne Ippolito
Richard Janda
Max Jarvie
Tyler Kolody
Sekoul Krastev
Tegan Maharaj
Robert Obryk
Dan Pilat
Valérie Pisano
Benjamin Prud’homme
Meng Qu
Nasim Rahaman
Irina Rish
Jean-François Rousseau
Victor Schmidt … (see 5 more)
Abhinav Sharma
Brooke Struck
Jian Tang
Martin Weiss
Yun William Yu
The SARS-CoV-2 (Covid-19) pandemic has caused significant strain on public health institutions around the world. Contact tracing is an essen… (see more)tial tool to change the course of the Covid-19 pandemic. Manual contact tracing of Covid-19 cases has significant challenges that limit the ability of public health authorities to minimize community infections. Personalized peer-to-peer contact tracing through the use of mobile apps has the potential to shift the paradigm. Some countries have deployed centralized tracking systems, but more privacy-protecting decentralized systems offer much of the same benefit without concentrating data in the hands of a state authority or for-profit corporations. Machine learning methods can circumvent some of the limitations of standard digital tracing by incorporating many clues and their uncertainty into a more graded and precise estimation of infection risk. The estimated risk can provide early risk awareness, personalized recommendations and relevant information to the user. Finally, non-identifying risk data can inform epidemiological models trained jointly with the machine learning predictor. These models can provide statistical evidence for the importance of factors involved in disease transmission. They can also be used to monitor, evaluate and optimize health policy and (de)confinement scenarios according to medical and economic productivity indicators. However, such a strategy based on mobile apps and machine learning should proactively mitigate potential ethical and privacy risks, which could have substantial impacts on society (not only impacts on health but also impacts such as stigmatization and abuse of personal data). Here, we present an overview of the rationale, design, ethical considerations and privacy strategy of `COVI,' a Covid-19 public peer-to-peer contact tracing and risk awareness mobile application developed in Canada.
2019-12-31
International Conference on Machine Learning (published)
doi.org
arxiv.org
Divergent protein-coding genes and brain size in primates
Malesys Bourgeron Dumas
Guillaume Dumas
Simon Malesys
Thomas Bourgeron
17 The human brain differs from that of other primates, but the genetic basis of these differences 18 remains unclear. We investigated the e… (see more)volutionary pressures acting on almost all human 19 protein-coding genes ( N =11,667; 1:1 orthologs in primates) on the basis of their divergence 20 from those of early hominins, such as Neanderthals, and non-human primates. We confirm 21 that genes encoding brain-related proteins are among the most strongly conserved protein- 22 coding genes in the human genome. Combining our evolutionary pressure metrics for the 23 protein-coding genome with recent datasets, we found that this conservation applied to genes 24 functionally associated with the synapse and expressed in brain structures such as the 25 prefrontal cortex and the cerebellum. Conversely, several of the protein-coding genes that 26 diverge most in hominins relative to other primates are associated with brain-associated 27 diseases, such as micro/macrocephaly, dyslexia, and autism. We also showed that cerebellum 28 granule neurons express a set of divergent protein-coding genes that may have contributed to 29 the emergence of fine motor skills and social cognition in humans. This resource is available 30 from http://neanderthal.pasteur.fr and can be used to estimate evolutionary constraints acting 31 on a set of genes and to explore their relative contributions to human traits. 32
2019-12-31
(published)
www.semanticscholar.org
On Efficiency in Hierarchical Reinforcement Learning
Zheng Wen
Doina Precup
Morteza Ibrahimi
Andre Barreto
Benjamin Van Roy
Satinder Singh
Hierarchical Reinforcement Learning (HRL) approaches promise to provide more efficient solutions to sequential decision making problems, bo… (see more)th in terms of statistical as well as computational efficiency. While this has been demonstrated empirically over time in a variety of tasks, theoretical results quantifying the ben-efits of such methods are still few and far between. In this paper, we discuss the kind of structure in a Markov decision process which gives rise to efficient HRL methods. Specifically, we formalize the intuition that HRL can exploit well repeating "subMDPs", with similar reward and transition structure. We show that, under reasonable assumptions, a model-based Thompson sampling-style HRL algorithm that exploits this structure is statistically efficient, as established through a finite-time regret bound. We also establish conditions under which planning with structure-induced options is near-optimal and computationally efficient.
2019-12-31
Advances in Neural Information Processing Systems 33 (NeurIPS 2020) (published)
dblp.uni-trier.de
Electric Vehicles Equilibrium Model that Considers Queue Delay and Mixed Traffic
Nurit Oliker
Miguel F. Anjos
Margarida Carvalho
Emma Frejinger
Bernard Gendron
This study develops an equilibrium model for electric vehicles (EVs) that considers both queue delays in charging stations and flow dependen… (see more)t travel times. This is a user equilibrium model that accounts for travel, charging and queuing time in the path choice modelling of EVs and the complementary traffic. Waiting and service times in charging stations are represented by an m/m/k queuing system. The model considers multiple vehicle and driver classes, expressing different battery capacity, initial charge state and range anxiety level. Feasible paths are found for multiple classes given their limited travel range. A numerical application exemplifies the limitations of EVs assignment and their impact on flow distribution.
2019-12-31
(published)
www.semanticscholar.org
Empowering Clinicians with MeDT: A Framework for Sepsis Treatment
Aamer Abdul Rahman
Pranav Agarwal
Vincent Michalski
Rita Noumeir
Philippe Jouvet
Samira Ebrahimi Kahou
2019-12-31
International Conference on Machine Learning (published)
openreview.net
openreview.net
AN ENSEMBLE APPROACH FOR DETECTING MACHINE FAILURE FROM SOUND Technical
Faruk Ahmed
Phong Cao Nguyen
Aaron Courville
We develop an ensemble-based approach for our submission to the anomaly detection challenge at DCASE 2020. The main members of our ensemble … (see more)are auto-encoders (with reconstruction error as the signal), classifiers (with negative predictive confidence as the signal), mismatch of the time-shifted signal with its Fourier-phase-shifted version, and a Gaussian mixture model on a set of common short-term features extracted from the waveform. The scores are passed through an exponential non-linearity and weighted to provide the final score, where the weighting and scaling hyper-parameters are learned on the development set. Our ensemble improves over the baseline on the development set.
2019-12-31
(published)
www.semanticscholar.org
Exploring Uncertainty Measures in Deep Networks for Multiple Sclerosis Lesion Detection and Segmentation
Tanya Nair
Doina Precup
Douglas L. Arnold
Tal Arbel
Deep learning (DL) networks have recently been shown to outperform other segmentation methods on various public, medical-image challenge dat… (see more)asets [3,11,16], especially for large pathologies. However, in the context of diseases such as Multiple Sclerosis (MS), monitoring all the focal lesions visible on MRI sequences, even very small ones, is essential for disease staging, prognosis, and evaluating treatment efficacy. Moreover, producing deterministic outputs hinders DL adoption into clinical routines. Uncertainty estimates for the predictions would permit subsequent revision by clinicians. We present the first exploration of multiple uncertainty estimates based on Monte Carlo (MC) dropout [4] in the context of deep networks for lesion detection and segmentation in medical images. Specifically, we develop a 3D MS lesion segmentation CNN, augmented to provide four different voxel-based uncertainty measures based on MC dropout. We train the network on a proprietary, large-scale, multi-site, multi-scanner, clinical MS dataset, and compute lesion-wise uncertainties by accumulating evidence from voxel-wise uncertainties within detected lesions. We analyze the performance of voxel-based segmentation and lesion-level detection by choosing operating points based on the uncertainty. Empirical evidence suggests that uncertainty measures consistently allow us to choose superior operating points compared only using the network's sigmoid output as a probability.
2019-12-31
Medical Image Analysis (published)
doi.org
arxiv.org
Fairness in Kidney Exchange Programs through Optimal Solutions Enumeration
Golnoosh Farnadi
Behrouz Babaki
Margarida Carvalho
Not all patients who need kidney transplant can find a donor with compatible characteristics. Kidney exchange programs (KEPs) seek to match … (see more)such incompatible patient-donor pairs together, usually with the objective of maximizing the total number of transplants. We propose a randomized policy for selecting an optimal solution in which patients’ equity of opportunity to receive a transplant is promoted. Our approach gives rise to the problem of enumerating all optimal solutions, which we tackle using a hybrid of constraint programming and linear programming. We empirically demonstrate the advantages of our proposed method over the common practice of using the first optimal solution obtained by a solver.
2019-12-31
(published)
www.semanticscholar.org
Finding and Visualizing Weaknesses of Deep Reinforcement Learning Agents
Christian Rupprecht
Cyril Ibrahim
Christopher Pal
As deep reinforcement learning driven by visual perception becomes more widely used there is a growing need to better understand and probe t… (see more)he learned agents. Understanding the decision making process and its relationship to visual inputs can be very valuable to identify problems in learned behavior. However, this topic has been relatively under-explored in the research community. In this work we present a method for synthesizing visual inputs of interest for a trained agent. Such inputs or states could be situations in which specific actions are necessary. Further, critical states in which a very high or a very low reward can be achieved are often interesting to understand the situational awareness of the system as they can correspond to risky states. To this end, we learn a generative model over the state space of the environment and use its latent space to optimize a target function for the state of interest. In our experiments we show that this method can generate insights for a variety of environments and reinforcement learning methods. We explore results in the standard Atari benchmark games as well as in an autonomous driving simulator. Based on the efficiency with which we have been able to identify behavioural weaknesses with this technique, we believe this general approach could serve as an important tool for AI safety applications.
2019-12-31
ICLR.cc/2020/Conference (poster)
openreview.net
openreview.net
Finding the needle in a high-dimensional haystack: Canonical correlation analysis for neuroscientists
Hao-Ting Wang
Jonathan Smallwood
Janaina Mourao-Miranda
Cedric Huchuan Xia
Theodore D. Satterthwaite
Danielle S. Bassett
Danilo Bzdok
2019-12-31
NeuroImage (published)
doi.org
Graph Density-Aware Losses for Novel Compositions in Scene Graph Generation
Boris Knyazev
Harm de Vries
Cătălina Cangea
Graham W. Taylor
Aaron Courville
Eugene Belilovsky
Scene graph generation (SGG) aims to predict graph-structured descriptions of input images, in the form of objects and relationships between… (see more) them. This task is becoming increasingly useful for progress at the interface of vision and language. Here, it is important - yet challenging - to perform well on novel (zero-shot) or rare (few-shot) compositions of objects and relationships. In this paper, we identify two key issues that limit such generalization. Firstly, we show that the standard loss used in this task is unintentionally a function of scene graph density. This leads to the neglect of individual edges in large sparse graphs during training, even though these contain diverse few-shot examples that are important for generalization. Secondly, the frequency of relationships can create a strong bias in this task, such that a blind model predicting the most frequent relationship achieves good performance. Consequently, some state-of-the-art models exploit this bias to improve results. We show that such models can suffer the most in their ability to generalize to rare compositions, evaluating two different models on the Visual Genome dataset and its more recent, improved version, GQA. To address these issues, we introduce a density-normalized edge loss, which provides more than a two-fold improvement in certain generalization metrics. Compared to other works in this direction, our enhancements require only a few lines of code and no added computational cost. We also highlight the difficulty of accurately evaluating models using existing metrics, especially on zero/few shots, and introduce a novel weighted metric.
2019-12-31
Proceedings of the British Machine Vision Conference 2020 (published)
doi.org
arxiv.org
GraphMix: Improved Training of Graph Neural Networks for Semi-Supervised Learning
Vikas Verma
Meng Qu
Alex Lamb
Yoshua Bengio
Juho Kannala
Jian Tang
We present GraphMix , a regularized training scheme for Graph Neural Network based semi-supervised object classification, leveraging the re… (see more)cent advances in the regularization of classical deep neural networks. Specifically, we pro-pose a unified approach in which we train a fully-connected network jointly with the graph neural network via parameter sharing, interpolation-based regularization and self-predicted-targets. Our proposed method is architecture agnostic in the sense that it can be applied to any variant of graph neural networks which applies a parametric transformation to the features of the graph nodes. Despite its simplicity, with GraphMix we can consistently improve results and achieve or closely match state-of-the-art performance using even simpler architectures such as Graph Convolutional Networks, across three established graph benchmarks: Cora, Citeseer and Pubmed citation network datasets, as well as three newly proposed datasets :Cora-Full, Co-author-CS and Co-author-Physics.
2019-12-31
(published)
www.semanticscholar.org