Publications

SPeCiaL: Self-Supervised Pretraining for Continual Learning

Lucas Caccia

Joelle Pineau

2022-09-28

Continual Semi-Supervised Learning (publié)

doi.org

arxiv.org

From analytic to synthetic-organizational pluralisms: A pluralistic enactive psychiatry

Christophe Gauld

Kristopher Nielsen

Manon Job

Hugo Bottemanne

Guillaume Dumas

2022-09-27

Frontiers in Psychiatry (publié)

doi.org

Estimating individual treatment effect on disability progression in multiple sclerosis using deep learning

Jean-Pierre R. Falet

Joshua D. Durso-Finley

Brennan Nichyporuk

Julien Schroeter

Francesca Bovis

Maria-Pia Sormani

Doina Precup

Tal Arbel

Douglas Arnold

2022-09-26

Nature Communications (publié)

doi.org

FedShuffle: Recipes for Better Use of Local Work in Federated Learning

Samuel Horváth

Maziar Sanjabi

Lin Xiao

Peter Richtárik

Michael Rabbat

The practice of applying several local updates before aggregation across clients has been empirically shown to be a successful approach to o… (voir plus)vercoming the communication bottleneck in Federated Learning (FL). Such methods are usually implemented by having clients perform one or more epochs of local training per round while randomly reshuffling their finite dataset in each epoch. Data imbalance, where clients have different numbers of local training samples, is ubiquitous in FL applications, resulting in different clients performing different numbers of local updates in each round. In this work, we propose a general recipe, FedShuffle, that better utilizes the local updates in FL, especially in this regime encompassing random reshuffling and heterogeneity. FedShuffle is the first local update method with theoretical convergence guarantees that incorporates random reshuffling, data imbalance, and client sampling — features that are essential in large-scale cross-device FL. We present a comprehensive theoretical analysis of FedShuffle and show, both theoretically and empirically, that it does not suffer from the objective function mismatch that is present in FL methods that assume homogeneous updates in heterogeneous FL setups, such as FedAvg (McMahan et al., 2017). In addition, by combining the ingredients above, FedShuffle improves upon FedNova (Wang et al., 2020), which was previously proposed to solve this mismatch. Similar to Mime (Karimireddy et al., 2020), we show that FedShuffle with momentum variance reduction (Cutkosky & Orabona, 2019) improves upon non-local methods under a Hessian similarity assumption.

2022-09-26

TMLR (accepté)

doi.org

openreview.net

The 5-year longitudinal diagnostic profile and health services utilization of patients treated with electroconvulsive therapy in Quebec: a population-based study

Simon Lafrenière

Fatemeh Gholi-Zadeh-Kharrat

Caroline Sirois

Victoria Massamba

Louis Rochette

Camille Brousseau-Paradis

Simon Patry

Christian Gagné

Morgane Lemasson

Geneviève Gariépy

Chantal Mérette

Elham Rahme

Alain Lesage

2022-09-26

Social Psychiatry and Psychiatric Epidemiology (publié)

doi.org

OSSEM: one-shot speaker adaptive speech enhancement using meta learning

Cheng Yu

Szu-Wei Fu

Tsun-An Hsieh

Yu Tsao

Mirco Ravanelli

2022-09-18

Interspeech 2022 (publié)

doi.org

arxiv.org

SoundChoice: Grapheme-to-Phoneme Models with Semantic Disambiguation

Artem Ploujnikov

Mirco Ravanelli

2022-09-18

Interspeech 2022 (publié)

doi.org

arxiv.org

Intervertebral Disc Labeling With Learning Shape Information, A Look Once Approach

Reza Azad

Moein Heidari

Julien Cohen-Adad

Ehsan Adeli

Dorit Merhof

Accurate and automatic segmentation of intervertebral discs from medical images is a critical task for the assessment of spine-related disea… (voir plus)ses such as osteoporosis, vertebral fractures, and intervertebral disc herniation. To date, various approaches have been developed in the literature which routinely relies on detecting the discs as the primary step. A disadvantage of many cohort studies is that the localization algorithm also yields false-positive detections. In this study, we aim to alleviate this problem by proposing a novel U-Net-based structure to predict a set of candidates for intervertebral disc locations. In our design, we integrate the image shape information (image gradients) to encourage the model to learn rich and generic geometrical information. This additional signal guides the model to selectively emphasize the contextual representation and suppress the less discriminative features. On the post-processing side, to further decrease the false positive rate, we propose a permutation invariant 'look once' model, which accelerates the candidate recovery procedure. In comparison with previous studies, our proposed approach does not need to perform the selection in an iterative fashion. The proposed method was evaluated on the spine generic public multi-center dataset and demonstrated superior performance compared to previous work. We have provided the implementation code in https://github.com/rezazad68/intervertebral-lookonce

2022-09-16

Predictive Intelligence in Medicine (publié)

doi.org

arxiv.org

Video Game Bad Smells: What They Are and How Developers Perceive Them

Vittoria Nardone

Biruk Asmare Muse

Mouna Abidi

Foutse Khomh

Massimiliano Di Penta

Video games represent a substantial and increasing share of the software market. However, their development is particularly challenging as i… (voir plus)t requires multi-faceted knowledge, which is not consolidated in computer science education yet. This article aims at defining a catalog of bad smells related to video game development. To achieve this goal, we mined discussions on general-purpose and video game-specific forums. After querying such a forum, we adopted an open coding strategy on a statistically significant sample of 572 discussions, stratified over different forums. As a result, we obtained a catalog of 28 bad smells, organized into five categories, covering problems related to game design and logic, physics, animation, rendering, or multiplayer. Then, we assessed the perceived relevance of such bad smells by surveying 76 game development professionals. The survey respondents agreed with the identified bad smells but also provided us with further insights about the discussed smells. Upon reporting results, we discuss bad smell examples, their consequences, as well as possible mitigation/fixing strategies and trade-offs to be pursued by developers. The catalog can be used not only as a guideline for developers and educators but also can pave the way toward better automated tool support for video game developers.

2022-09-15

ACM Transactions on Software Engineering and Methodology (publié)

doi.org

Measuring Commonality in Recommendation of Cultural Content: Recommender Systems to Enhance Cultural Citizenship

Andres Ferraro

Gustavo Ferreira

Fernando Diaz

Georgina Born

2022-09-13

Proceedings of the 16th ACM Conference on Recommender Systems (publié)

doi.org

arxiv.org