Publications

Inherent privacy limitations of decentralized contact tracing apps
Yoshua Bengio
Daphne Ippolito
Richard Janda
Max Jarvie
Benjamin Prud'homme
Jean-François Rousseau
Abhinav Sharma
Yun William Yu
2021-01-11
Journal of the American Medical Informatics Association : JAMIA (published)
doi.org
A Universal Representation Transformer Layer for Few-Shot Image Classification
Li Li
William L. Hamilton
Guodong Long
Jing Jiang
Hugo Larochelle
Few-shot classification aims to recognize unseen classes when presented with only a small number of samples. We consider the problem of mult… (see more)i-domain few-shot image classification, where unseen classes and examples come from diverse data sources. This problem has seen growing interest and has inspired the development of benchmarks such as Meta-Dataset. A key challenge in this multi-domain setting is to effectively integrate the feature representations from the diverse set of training domains. Here, we propose a Universal Representation Transformer (URT) layer, that meta-learns to leverage universal features for few-shot classification by dynamically re-weighting and composing the most appropriate domain-specific representations. In experiments, we show that URT sets a new state-of-the-art result on Meta-Dataset. Specifically, it achieves top-performance on the highest number of data sources compared to competing methods. We analyze variants of URT and present a visualization of the attention score heatmaps that sheds light on how the model performs cross-domain generalization.
2021-01-11
ICLR.cc/2021/Conference (poster)
openreview.net
openreview.net
Mass‐spectrometry analysis of the human pineal proteome during night and day and in autism
Guillaume Dumas
Hany Goubran‐Botros
Mariette Matondo
Cécile Pagan
Cyril Boulègue
Thibault Chaze
Julia Chamot‐Rooke
Erik Maronde
Thomas Bourgeron
The human pineal gland regulates day‐night dynamics of multiple physiological processes, especially through the secretion of melatonin. Us… (see more)ing mass‐spectrometry‐based proteomics and dedicated analysis tools, we identify proteins in the human pineal gland and analyze systematically their variation throughout the day and compare these changes in the pineal proteome between control specimens and donors diagnosed with autism. Results reveal diverse regulated clusters of proteins with, among others, catabolic carbohydrate process and cytoplasmic membrane‐bounded vesicle‐related proteins differing between day and night and/or control versus autism pineal glands. These data show novel and unexpected processes happening in the human pineal gland during the day/night rhythm as well as specific differences between autism donor pineal glands and those from controls.
2021-01-10
Journal of Pineal Research (published)
doi.org
Attention Based Pruning for Shift Networks
Ghouthi Boukli hacene
Carlos Lassance
Vincent Gripon
Matthieu Courbariaux
Yoshua Bengio
In many application domains such as computer vision, Convolutional Layers (CLs) are key to the accuracy of deep learning methods. However, i… (see more)t is often required to assemble a large number of CLs, each containing thousands of parameters, in order to reach state-of-the-art accuracy, thus resulting in complex and demanding systems that are poorly fitted to resource-limited devices. Recently, methods have been proposed to replace the generic convolution operator by the combination of a shift operation and a simpler 1x1 convolution. The resulting block, called Shift Layer (SL), is an efficient alternative to CLs in the sense it allows to reach similar accuracies on various tasks with faster computations and fewer parameters. In this contribution, we introduce Shift Attention Layers (SALs), which extend SLs by using an attention mechanism that learns which shifts are the best at the same time the network function is trained. We demonstrate SALs are able to outperform vanilla SLs (and CLs) on various object recognition benchmarks while significantly reducing the number of float operations and parameters for the inference.
2021-01-09
International Conference on Pattern Recognition (published)
doi.org
arxiv.org
The patient advisor, an organizational resource as a lever for an enhanced oncology patient experience (PAROLE-onco): a longitudinal multiple case study protocol
M. P. Pomey
M. de Guise
M. Desforges
K. Bouchard
C. Vialaron
L. Normandin
M. Iliescu-Nelea
I. Fortin
I. Ganache
C. Régis
Z. Rosberger
D. Charpentier
L. Bélanger
M. Dorval
D. P. Ghadiri
M. Lavoie-Tremblay
A. Boivin
J. F. Pelletier
N. Fernandez
A. M. Danino
Quebec is one of the Canadian provinces with the highest rates of cancer incidence and prevalence. A study by the Rossy Cancer Network (RCN)… (see more) of McGill university assessed six aspects of the patient experience among cancer patients and found that emotional support is the aspect most lacking. To improve this support, trained patient advisors (PAs) can be included as full-fledged members of the healthcare team, given that PA can rely on their knowledge with experiencing the disease and from using health and social care services to accompany cancer patients, they could help to round out the health and social care services offer in oncology. However, the feasibility of integrating PAs in clinical oncology teams has not been studied. In this multisite study, we will explore how to integrate PAs in clinical oncology teams and, under what conditions this can be successfully done. We aim to better understand effects of this PA intervention on patients, on the PAs themselves, the health and social care team, the administrators, and on the organization of services and to identify associated ethical and legal issues. We will conduct six mixed methods longitudinal case studies. Qualitative data will be used to study the integration of the PAs into clinical oncology teams and to identify the factors that are facilitators and inhibitors of the process, the associated ethical and legal issues, and the challenges that the PAs experience. Quantitative data will be used to assess effects on patients, PAs and team members, if any, of the PA intervention. The results will be used to support oncology programs in the integration of PAs into their healthcare teams and to design a future randomized pragmatic trial to evaluate the impact of PAs as full-fledged members of clinical oncology teams on cancer patients’ experience of emotional support throughout their care trajectory. This study will be the first to integrate PAs as full-fledged members of the clinical oncology team and to assess possible clinical and organizational level effects. Given the unique role of PAs, this study will complement the body of research on peer support and patient navigation. An additional innovative aspect of this study will be consideration of the ethical and legal issues at stake and how to address them in the health care organizations.
2021-01-03
BMC Health Services Research (published)
doi.org
Seeing Through your Skin: Recognizing Objects with a Novel Visuotactile Sensor
Francois Hogan
M. Jenkin
Sahand Rezaei-Shoshtari
Yogesh Girdhar
David Meger
Gregory Dudek
We introduce a new class of vision-based sensor and associated algorithmic processes that combine visual imaging with high-resolution tactil… (see more)e sending, all in a uniform hardware and computational architecture. We demonstrate the sensor’s efficacy for both multi-modal object recognition and metrology. Object recognition is typically formulated as an unimodal task, but by combining two sensor modalities we show that we can achieve several significant performance improvements. This sensor, named the See-Through-your-Skin sensor (STS), is designed to provide rich multi-modal sensing of contact surfaces. Inspired by recent developments in optical tactile sensing technology, we address a key missing feature of these sensors: the ability to capture a visual perspective of the region beyond the contact surface. Whereas optical tactile sensors are typically opaque, we present a sensor with a semitransparent skin that has the dual capabilities of acting as a tactile sensor and/or as a visual camera depending on its internal lighting conditions. This paper details the design of the sensor, showcases its dual sensing capabilities, and presents a deep learning architecture that fuses vision and touch. We validate the ability of the sensor to classify household objects, recognize fine textures, and infer their physical properties both through numerical simulations and experiments with a smart countertop prototype.
2021-01-02
2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (published)
doi.org
arxiv.org
Assessing the Impact: Does an Improvement to a Revenue Management System Lead to an Improved Revenue?
Greta Laage
Emma Frejinger
William L. Hamilton
Andrea Lodi
Guillaume Rabusseau
Airlines and other industries have been making use of sophisticated Revenue Management Systems to maximize revenue for decades. While improv… (see more)ing the different components of these systems has been the focus of numerous studies, estimating the impact of such improvements on the revenue has been overlooked in the literature despite its practical importance. Indeed, quantifying the benefit of a change in a system serves as support for investment decisions. This is a challenging problem as it corresponds to the difference between the generated value and the value that would have been generated keeping the system as before. The latter is not observable. Moreover, the expected impact can be small in relative value. In this paper, we cast the problem as counterfactual prediction of unobserved revenue. The impact on revenue is then the difference between the observed and the estimated revenue. The originality of this work lies in the innovative application of econometric methods proposed for macroeconomic applications to a new problem setting. Broadly applicable, the approach benefits from only requiring revenue data observed for origin-destination pairs in the network of the airline at each day, before and after a change in the system is applied. We report results using real large-scale data from Air Canada. We compare a deep neural network counterfactual predictions model with econometric models. They achieve respectively 1% and 1.1% of error on the counterfactual revenue predictions, and allow to accurately estimate small impacts (in the order of 2%).
2020-12-31
arXiv.org (preprint)
doi.org
arxiv.org
C AUSAL R: Causal Reasoning over Natural Language Rulebases
Jason Weston
Antoine Bordes
Sumit Chopra
Thomas Wolf
Lysandre Debut
Julien Victor Sanh
Clement Chaumond
Anthony Delangue
Pier-339 Moi
Tim ric Cistac
R´emi Rault
Morgan Louf
Funtow-900 Joe
Sam Davison
Patrick Shleifer
Von Platen
Clara Ma
Yacine Jernite
Julien Plu
Canwen Xu … (see 6 more)
Zhilin Yang
Peng Qi
Saizheng Zhang
Yoshua Bengio
William W Cohen
Russ Salakhutdinov
Transformers have been shown to be able to 001 perform deductive reasoning on a logical rule-002 base containing rules and statements writte… (see more)n 003 in natural language. Recent works show that 004 such models can also produce the reasoning 005 steps (i.e., the proof graph ) that emulate the 006 model’s logical reasoning process. But these 007 models behave as a black-box unit that emu-008 lates the reasoning process without any causal 009 constraints in the reasoning steps, thus ques-010 tioning the faithfulness. In this work, we frame 011 the deductive logical reasoning task as a causal 012 process by defining three modular components: 013 rule selection, fact selection, and knowledge 014 composition. The rule and fact selection steps 015 select the candidate rule and facts to be used 016 and then the knowledge composition combines 017 them to generate new inferences. This ensures 018 model faithfulness by assured causal relation 019 from the proof step to the inference reasoning. 020 To test our causal reasoning framework, we 021 propose C AUSAL R where the above three com-022 ponents are independently modeled by trans-023 formers. We observe that C AUSAL R is robust 024 to novel language perturbations, and is com-025 petitive with previous works on existing rea-026 soning datasets. Furthermore, the errors made 027 by C AUSAL R are more interpretable due to 028 the multi-modular approach compared to black-029 box generative models. 1 030
2020-12-31
(published)
www.semanticscholar.org
Automatic Fall Risk Detection based on Imbalanced Data
Yen-Hung Liu
Ye Liu
Patrick C. K. Hung
Farkhund Iqbal
Benjamin C. M. Fung
In recent years, the declining birthrate and aging population have gradually brought countries into an ageing society. Regarding accidents t… (see more)hat occur amongst the elderly, falls are an essential problem that quickly causes indirect physical loss. In this paper, we propose a pose estimation-based fall detection algorithm to detect fall risks. We use body ratio, acceleration and deflection as key features instead of using the body keypoints coordinates. Since fall data is rare in real-world situations, we train and evaluate our approach in a highly imbalanced data setting. We assess not only different imbalanced data handling methods but also different machine learning algorithms. After oversampling on our training data, the K-Nearest Neighbors (KNN) algorithm achieves the best performance. The F1 scores for three different classes, Normal, Fall, and Lying, are 1.00, 0.85 and 0.96, which is comparable to previous research. The experiment shows that our approach is more interpretable with the key feature from skeleton information. Moreover, it can apply in multi-people scenarios and has robustness on medium occlusion.
2020-12-31
IEEE Access (published)
doi.org
BabyAI 1.1
David Y. T. Hui
Maxime Chevalier-Boisvert
Dzmitry Bahdanau
Yoshua Bengio
The BabyAI platform is designed to measure the sample efficiency of training an agent to follow grounded-language instructions. BabyAI 1.0 p… (see more)resents baseline results of an agent trained by deep imitation or reinforcement learning. BabyAI 1.1 improves the agent's architecture in three minor ways. This increases reinforcement learning sample efficiency by up to 3 times and improves imitation learning performance on the hardest level from 77 % to 90.4 %. We hope that these improvements increase the computational efficiency of BabyAI experiments and help users design better agents.
2020-12-31
(published)
arxiv.org
arxiv.org
CAMAP: Artificial neural networks unveil the role of 1 codon arrangement in modulating MHC-I peptides 2 presentation discovery of minor histocompatibility with
Tariq Daouda
Maude Dumont-Lagacé
Albert Feghaly
Yahya Benslimane
6. Rébecca
Panes
Mathieu Courcelles
Mohamed Benhammadi
Lea Harrington
Pierre Thibault
François Major
Yoshua Bengio
Étienne Gagnon
S. Lemieux
Claude Perreault
30 MHC-I associated peptides (MAPs) play a central role in the elimination of virus-infected and 31 neoplastic cells by CD8 T cells. However… (see more), accurately predicting the MAP repertoire remains 32 difficult, because only a fraction of the transcriptome generates MAPs. In this study, we 33 investigated whether codon arrangement (usage and placement) regulates MAP biogenesis. We 34 developed an artificial neural network called Codon Arrangement MAP Predictor (CAMAP), 35 predicting MAP presentation solely from mRNA sequences flanking the MAP-coding codons 36 (MCCs), while excluding the MCC per se . CAMAP predictions were significantly more accurate 37 when using original codon sequences than shuffled codon sequences which reflect amino acid 38 usage. Furthermore, predictions were independent of mRNA expression and MAP binding affinity 39 to MHC-I molecules and applied to several cell types and species. Combining MAP ligand scores, 40 transcript expression level and CAMAP scores was particularly useful to increaser MAP prediction 41 accuracy. Using an in vitro assay, we showed that varying the synonymous codons in the regions 42 flanking the MCCs (without changing the amino acid sequence) resulted in significant modulation 43 of MAP presentation at the cell surface. Taken together, our results demonstrate the role of codon 44 arrangement in the regulation of MAP presentation and support integration of both translational 45 and post-translational events in predictive algorithms to ameliorate modeling of the 46 immunopeptidome. 47 48 49 they modulated the levels of SIINFEKL presentation in both constructs, but enhanced translation efficiency could only be detected for OVA-RP. These data show that codon arrangement can modulate MAP presentation strength without any changes in the amino
2020-12-31
(published)
www.semanticscholar.org
CAMAP: Artificial neural networks unveil the role of codon arrangement in modulating MHC-I peptides presentation
Tariq Daouda
Maude Dumont-Lagacé
Albert Feghaly
Yahya Benslimane
Rebecca Panes
Mathieu Courcelles
Mohamed Benhammadi
Lea Harrington
Pierre Thibault
François Major
Yoshua Bengio
Étienne Gagnon
Sébastien Lemieux
Claude Perreault
MHC-I associated peptides (MAPs) are small fragments of intracellular proteins presented at the surface of cells and used by the immune syst… (see more)em to detect and eliminate cancerous or virus-infected cells. While it is theoretically possible to predict which portions of the intracellular proteins will be naturally processed by the cells to ultimately reach the surface, current methodologies have prohibitively high false discovery rates. Here we introduce an artificial neural network called Codon Arrangement MAP Predictor (CAMAP) which integrates information from mRNA-to-protein translation to other factors regulating MAP biogenesis (e.g. MAP ligand score and transcript expression levels) to improve MAP prediction accuracy. While most MAP predictive approaches focus on MAP sequences per se, CAMAP’s novelty is to analyze the MAP-flanking mRNA sequences, thereby providing completely independent information for MAP prediction. We show on several datasets that the integration of CAMAP scores with other known factors involved in MAP presentation (i.e. MAP ligand score and mRNA expression) significantly improves MAP prediction accuracy, and further validate CAMAP learned features using anin-vitroassay. These findings may have major implications for the design of vaccines against cancers and viruses, and in times of pandemics could accelerate the identification of relevant MAPs of viral origins.
2020-12-31
PLoS Computational Biology (published)
doi.org