Publications

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… (voir plus)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
2020 25th International Conference on Pattern Recognition (ICPR) (publié)
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)… (voir plus) 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 (publié)
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… (voir plus)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) (publié)
doi.org
arxiv.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… (voir plus)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.
2020-12-31
Conference on Automated Knowledge Base Construction (publié)
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… (voir plus)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.
2020-12-31
MLSys (publié)
doi.org
arxiv.org
ADEPT: An Adjective-Dependent Plausibility Task
Ali Emami
Ian Porada
A.R. Olteanu
Kaheer Suleman
Adam Trischler
Jackie CK Cheung
2020-12-31
Annual Meeting of the Association for Computational Linguistics (publié)
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 … (voir plus)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.
2020-12-31
Advances in Neural Information Processing Systems 34 (NeurIPS 2021) (publié)
doi.org
openreview.net
An Analysis of the Adaptation Speed of Causal Models
Rémi Le Priol
Reza Babanezhad Harikandeh
Yoshua Bengio
Simon Lacoste-Julien
Consider a collection of datasets generated by unknown interventions on an unknown structural causal model …
2020-12-31
AISTATS (publié)
doi.org
proceedings.mlr.press
Analyzing the Contribution of Ethical Charters to Building the Future of Artificial Intelligence Governance
Lyse Langlois
Catherine Régis
2020-12-31
Reflections on Artificial Intelligence for Humanity (publié)
doi.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… (voir plus)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 (prépublication)
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 … (voir 6 de plus)
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… (voir plus)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
(publié)
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… (voir plus)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 (publié)
doi.org