Publications

Masked Siamese Networks for Label-Efficient Learning
Mahmoud Assran
Mathilde Caron
Ishan Misra
Piotr Bojanowski
Florian Bordes
Pascal Vincent
Armand Joulin
Michael Rabbat
Nicolas Ballas
We propose Masked Siamese Networks (MSN), a self-supervised learning framework for learning image representations. Our approach matches the … (voir plus)representation of an image view containing randomly masked patches to the representation of the original unmasked image. This self-supervised pre-training strategy is particularly scalable when applied to Vision Transformers since only the unmasked patches are processed by the network. As a result, MSNs improve the scalability of joint-embedding architectures, while producing representations of a high semantic level that perform competitively on low-shot image classification. For instance, on ImageNet-1K, with only 5,000 annotated images, our base MSN model achieves 72.4% top-1 accuracy, and with 1% of ImageNet-1K labels, we achieve 75.7% top-1 accuracy, setting a new state-of-the-art for self-supervised learning on this benchmark. Our code is publicly available.
2022-04-14
ArXiv (preprint)
doi.org
arxiv.org
Masked Siamese Networks for Label-Efficient Learning
Mahmoud Assran
Mathilde Caron
Ishan Misra
Piotr Bojanowski
Florian Bordes
Pascal Vincent
Armand Joulin
Michael Rabbat
Nicolas Ballas
We propose Masked Siamese Networks (MSN), a self-supervised learning framework for learning image representations. Our approach matches the … (voir plus)representation of an image view containing randomly masked patches to the representation of the original unmasked image. This self-supervised pre-training strategy is particularly scalable when applied to Vision Transformers since only the unmasked patches are processed by the network. As a result, MSNs improve the scalability of joint-embedding architectures, while producing representations of a high semantic level that perform competitively on low-shot image classification. For instance, on ImageNet-1K, with only 5,000 annotated images, our base MSN model achieves 72.4% top-1 accuracy, and with 1% of ImageNet-1K labels, we achieve 75.7% top-1 accuracy, setting a new state-of-the-art for self-supervised learning on this benchmark. Our code is publicly available.
2022-04-14
ArXiv (prépublication)
doi.org
arxiv.org
Microscopy analysis neural network to solve detection, enumeration and segmentation from image-level annotations
Anthony Bilodeau
Constantin V. L. Delmas
M. Parent
Paul De Koninck
Audrey Durand
Flavie Lavoie-Cardinal
2022-04-14
Nature Machine Intelligence (published)
doi.org
Microscopy analysis neural network to solve detection, enumeration and segmentation from image-level annotations
Anthony Bilodeau
Constantin V.L. Delmas
Martin Parent
Paul De Koninck
Audrey Durand
Flavie Lavoie-Cardinal
2022-04-14
Nature Machine Intelligence (publié)
doi.org
User Experience of a Computer-Based Decision Aid for Prenatal Trisomy Screening: Mixed Methods Explanatory Study
Titilayo Tatiana Agbadje
Chantale Pilon
Pierre Bérubé
Jean‐claude Forest
François Rousseau
Samira Abbasgholizadeh-Rahimi
Yves Giguère
France Légaré
Background Mobile health tools can support shared decision-making. We developed a computer-based decision aid (DA) to help pregnant women an… (voir plus)d their partners make informed, value-congruent decisions regarding prenatal screening for trisomy. Objective This study aims to assess the usability and usefulness of computer-based DA among pregnant women, clinicians, and policy makers. Methods For this mixed methods sequential explanatory study, we planned to recruit a convenience sample of 45 pregnant women, 45 clinicians from 3 clinical sites, and 15 policy makers. Eligible women were aged >18 years and >16 weeks pregnant or had recently given birth. Eligible clinicians and policy makers were involved in prenatal care. We asked the participants to navigate a computer-based DA. We asked the women about the usefulness of the DA and their self-confidence in decision-making. We asked all participants about usability, quality, acceptability, satisfaction with the content of the DA, and collected sociodemographic data. We explored participants’ reactions to the computer-based DA and solicited suggestions. Our interview guide was based on the Mobile App Rating Scale. We performed descriptive analyses of the quantitative data and thematic deductive and inductive analyses of the qualitative data for each participant category. Results A total of 45 pregnant women, 14 clinicians, and 8 policy makers participated. Most pregnant women were aged between 25 and 34 years (34/45, 75%) and White (42/45, 94%). Most clinicians were aged between 35 and 44 years (5/14, 36%) and women (11/14, 79%), and all were White (14/14, 100%); the largest proportion of policy makers was aged between 45 and 54 years (4/8, 50%), women (5/8, 62%), and White (8/8, 100%). The mean usefulness score for preparing for decision-making for women was 80/100 (SD 13), and the mean self-efficacy score was 88/100 (SD 11). The mean usability score was 84/100 (SD 14) for pregnant women, 77/100 (SD 14) for clinicians, and 79/100 (SD 23) for policy makers. The mean global score for quality was 80/100 (SD 9) for pregnant women, 72/100 (SD 12) for clinicians, and 80/100 (SD 9) for policy makers. Regarding acceptability, participants found the amount of information just right (52/66, 79%), balanced (58/66, 88%), useful (38/66, 58%), and sufficient (50/66, 76%). The mean satisfaction score with the content was 84/100 (SD 13) for pregnant women, 73/100 (SD 16) for clinicians, and 73/100 (SD 20) for policy makers. Participants thought the DA could be more engaging (eg, more customizable) and suggested strategies for implementation, such as incorporating it into clinical guidelines. Conclusions Pregnant women, clinicians, and policy makers found the DA usable and useful. The next steps are to incorporate user suggestions for improving engagement and implementing the computer-based DA in clinical practice.
2022-04-14
JMIR Pediatrics and Parenting (published)
doi.org
User Experience of a Computer-Based Decision Aid for Prenatal Trisomy Screening: Mixed Methods Explanatory Study
Titilayo Tatiana Agbadje
Chantale Pilon
Pierre Bérubé
Jean-Claude Forest
François Rousseau
Samira Abbasgholizadeh-Rahimi
Yves Giguère
France Légaré
2022-04-14
JMIR Pediatrics and Parenting (publié)
doi.org
Aspirations and Practice of ML Model Documentation: Moving the Needle with Nudging and Traceability
Avinash Bhat
Austin Coursey
Grace Hu
Sixian Li
Nadia Nahar
Shurui Zhou
Christian Kästner
Jin Guo
The documentation practice for machine-learned (ML) models often falls short of established practices for traditional software, which impede… (voir plus)s model accountability and inadvertently abets inappropriate or misuse of models. Recently, model cards, a proposal for model documentation, have attracted notable attention, but their impact on the actual practice is unclear. In this work, we systematically study the model documentation in the field and investigate how to encourage more responsible and accountable documentation practice. Our analysis of publicly available model cards reveals a substantial gap between the proposal and the practice. We then design a tool named DocML aiming to (1) nudge the data scientists to comply with the model cards proposal during the model development, especially the sections related to ethics, and (2) assess and manage the documentation quality. A lab study reveals the benefit of our tool towards long-term documentation quality and accountability.
2022-04-13
ArXiv (preprint)
doi.org
arxiv.org
TopiOCQA: Open-domain Conversational Question Answering with Topic Switching
Vaibhav Adlakha
Shehzaad Dhuliawala
Kaheer Suleman
Harm de Vries
Siva Reddy
2022-04-13
Transactions of the Association for Computational Linguistics (publié)
doi.org
arxiv.org
A neurodynamic model of inter-brain coupling in the gamma band
Quentin Moreau
Lena Adel
Douglas Caitriona
Ranjbaran Ghazaleh
Guillaume Dumas
2022-04-11
bioRxiv (prépublication)
doi.org
Can neurogenesis act as a neural regularizer?
Lina M. Tran
Adam Santoro
Lulu Liu
Sheena A. Josselyn
Blake Richards
Paul W. Frankland
New neurons are continuously generated in the subgranular zone of the dentate gyrus throughout adulthood. These new neurons gradually integr… (voir plus)ate into hippocampal circuits, forming new naïve synapses. Viewed from this perspective, these new neurons may represent a significant source of ‘wiring’ noise in hippocampal networks. In machine learning, such noise injection is commonly used as a regularization technique. Regularization techniques help prevent overfitting training data, and allow models to generalize learning to new, unseen data. Using a computational modeling approach, here we ask whether a neurogenesis-like process similarly acts as a regularizer, facilitating generalization in a category learning task. In a convolutional neural network (CNN) trained on the CIFAR-10 object recognition dataset, we modeled neurogenesis as a replacement/turnover mechanism, where weights for a randomly chosen small subset of neurons in a chosen hidden layer were re-initialized to new values as the model learned to categorize 10 different classes of objects. We found that neurogenesis enhanced generalization on unseen test data compared to networks with no neurogenesis. Moreover, neurogenic networks either outperformed or performed similarly to networks with conventional noise injection (i.e., dropout, weight decay, and neural noise). These results suggest that neurogenesis can enhance generalization in hippocampal learning through noise-injection, expanding on the roles that neurogenesis may have in cognition. Author Summary In deep neural networks, various forms of noise injection are used as regularization techniques to prevent overfitting and promote generalization on unseen test data. Here, we were interested in whether adult neurogenesis– the lifelong production of new neurons in the hippocampus– might similarly function as a regularizer in the brain. We explored this question computationally, assessing whether implementing a neurogenesis-like process in a hidden layer within a convolutional neural network trained in a category learning task would prevent overfitting and promote generalization. We found that neurogenesis regularization was as least as effective as, or more effective than, conventional regularizers (i.e., dropout, weight decay and neural noise) in improving model performance. These results suggest that optimal levels of hippocampal neurogenesis may improve memory-guided decision making by preventing overfitting, thereby promoting the formation of more generalized memories that can be applied in a broader range of circumstances. We outline how these predictions may be evaluated behaviorally in rodents with altered hippocampal neurogenesis.
2022-04-10
bioRxiv (prépublication)
doi.org
Clones in deep learning code: what, where, and why?
Hadhemi Jebnoun
Md Saidur Rahman
Foutse Khomh
Biruk Asmare Muse
2022-04-08
Empirical Software Engineering (publié)
doi.org
arxiv.org
NeoRS: A Neonatal Resting State fMRI Data Preprocessing Pipeline
Vicente Enguix
Jeanette K. Kenley
David Luck
Julien Cohen-Adad
G. Lodygensky
Resting state functional MRI (rsfMRI) has been shown to be a promising tool to study intrinsic brain functional connectivity and assess its … (voir plus)integrity in cerebral development. In neonates, where functional MRI is limited to very few paradigms, rsfMRI was shown to be a relevant tool to explore regional interactions of brain networks. However, to identify the resting state networks, data needs to be carefully processed to reduce artifacts compromising the interpretation of results. Because of the non-collaborative nature of the neonates, the differences in brain size and the reversed contrast compared to adults due to myelination, neonates can’t be processed with the existing adult pipelines, as they are not adapted. Therefore, we developed NeoRS, a rsfMRI pipeline for neonates. The pipeline relies on popular neuroimaging tools (FSL, AFNI, and SPM) and is optimized for the neonatal brain. The main processing steps include image registration to an atlas, skull stripping, tissue segmentation, slice timing and head motion correction and regression of confounds which compromise functional data interpretation. To address the specificity of neonatal brain imaging, particular attention was given to registration including neonatal atlas type and parameters, such as brain size variations, and contrast differences compared to adults. Furthermore, head motion was scrutinized, and motion management optimized, as it is a major issue when processing neonatal rsfMRI data. The pipeline includes quality control using visual assessment checkpoints. To assess the effectiveness of NeoRS processing steps we used the neonatal data from the Baby Connectome Project dataset including a total of 10 neonates. NeoRS was designed to work on both multi-band and single-band acquisitions and is applicable on smaller datasets. NeoRS also includes popular functional connectivity analysis features such as seed-to-seed or seed-to-voxel correlations. Language, default mode, dorsal attention, visual, ventral attention, motor and fronto-parietal networks were evaluated. Topology found the different analyzed networks were in agreement with previously published studies in the neonate. NeoRS is coded in Matlab and allows parallel computing to reduce computational times; it is open-source and available on GitHub (https://github.com/venguix/NeoRS). NeoRS allows robust image processing of the neonatal rsfMRI data that can be readily customized to different datasets.
2022-04-08
ArXiv (preprint)
doi.org
arxiv.org