1.5k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Université de MontréalCA125 papers
- McGill UniversityCA59 papers
- Polytechnique MontréalCA27 papers
- Flatiron Health (United States)US24 papers
- Flatiron Institute23 papers
- Centre National de la Recherche ScientifiqueFR22 papers
- University of TorontoCA16 papers
- Canadian Institute for Advanced ResearchCA14 papers
- Université Paris-SaclayFR14 papers
- University of AmsterdamNL13 papers
- University of WaterlooCA13 papers
- Centre for Research in Astrophysics of QuébecCA12 papers
34 papers · 1 filter
Unpaired Modality Translation for Pseudo Labeling of Histology Images
Arthur Boschet, Armand Collin, Nishka Katoch +1
The segmentation of histological images is critical for various biomedical applications, yet the lack of annotated data presents a significant challenge. We propose a microscopy ps…
Error Diversity Matters: An Error-Resistant Ensemble Method for Unsupervised Dependency Parsing
Behzad Shayegh, Hobie H. -B. Lee, Xiaodan Zhu +2
We address unsupervised dependency parsing by building an ensemble of diverse existing models through post hoc aggregation of their output dependency parse structures. We observe t…
Classification Drives Geographic Bias in Street Scene Segmentation
Rahul Nair, Gabriel Tseng, Esther Rolf +2
Previous studies showed that image datasets lacking geographic diversity can lead to biased performance in models trained on them. While earlier work studied general-purpose image…
Robustness of Neural Ratio and Posterior Estimators to Distributional Shifts for Population-Level Dark Matter Analysis in Strong Gravitational Lensing
Andreas Filipp, Yashar Hezaveh, Laurence Perreault-Levasseur
We investigate the robustness of Neural Ratio Estimators (NREs) and Neural Posterior Estimators (NPEs) to distributional shifts in the context of measuring the abundance of dark ma…
Development and Comparative Analysis of Machine Learning Models for Hypoxemia Severity Triage in CBRNE Emergency Scenarios Using Physiological and Demographic Data from Medical-Grade Devices
Santino Nanini, Mariem Abid, Yassir Mamouni +3
This paper presents the development of machine learning (ML) models to predict hypoxemia severity during emergency triage, especially in Chemical, Biological, Radiological, Nuclear…
Gravitational-Wave Parameter Estimation in non-Gaussian noise using Score-Based Likelihood Characterization
Ronan Legin, Maximiliano Isi, Kaze W. K. Wong +2
Gravitational-wave (GW) parameter estimation typically assumes that instrumental noise is Gaussian and stationary. Obvious departures from this idealization are typically handled o…
Pairing Analogy-Augmented Generation with Procedural Memory for Procedural Q&A
K Roth, Rushil Gupta, Simon Halle +1
Large language models struggle to synthesize disparate pieces of information into a coherent plan when approaching a complex procedural task. In this work, we introduce a novel for…
A Unified Solution to Diverse Heterogeneities in One-shot Federated Learning
Jun Bai, Yiliao Song, Di Wu +6
One-Shot Federated Learning (OSFL) restricts communication between the server and clients to a single round, significantly reducing communication costs and minimizing privacy leaka…
Hallucination Detox: Sensitivity Dropout (SenD) for Large Language Model Training
Shahrad Mohammadzadeh, Juan David Guerra, Marco Bonizzato +2
As large language models (LLMs) become increasingly prevalent, concerns about their reliability, particularly due to hallucinations - factually inaccurate or irrelevant outputs - h…
Diversity of Thought Elicits Stronger Reasoning Capabilities in Multi-Agent Debate Frameworks
Mahmood Hegazy
Large language models (LLMs) excel in natural language generation but often confidently produce incorrect responses, especially in tasks like mathematical reasoning. Chain-of-thoug…
The Intrinsic Flattening of Galaxy Disks
Jeremy Favaro, Stéphane Courteau, Sébastien Comerón +1
Highly inclined (edge-on) disk galaxies offer the unique perspective to constrain their intrinsic flattening, , where and are respectively the vertical and long radial…
Revealing the Pitfalls and Re-Evaluating the Advancement of Heterophilic Graph Learning
Sitao Luan, Qincheng Lu, Chenqing Hua +3
Over the past decade, Graph Neural Networks (GNNs) have achieved great success on machine learning tasks with relational data. However, recent studies have found that heterophily c…
SCIsegV2: A Universal Tool for Segmentation of Intramedullary Lesions in Spinal Cord Injury
Enamundram Naga Karthik, Jan Valošek, Lynn Farner +10
Spinal cord injury (SCI) is a devastating incidence leading to permanent paralysis and loss of sensory-motor functions potentially resulting in the formation of lesions within the…
Tree semantic segmentation from aerial image time series
Venkatesh Ramesh, Arthur Ouaknine, David Rolnick
Earth's forests play an important role in the fight against climate change, and are in turn negatively affected by it. Effective monitoring of different tree species is essential t…
Social and Ethical Risks Posed by General-Purpose LLMs for Settling Newcomers in Canada
Isar Nejadgholi, Maryam Molamohammadi, Samir Bakhtawar
The non-profit settlement sector in Canada supports newcomers in achieving successful integration. This sector faces increasing operational pressures amidst rising immigration targ…
TrackPGD: Efficient Adversarial Attack using Object Binary Masks against Robust Transformer Trackers
Fatemeh Nourilenjan Nokabadi, Yann Batiste Pequignot, Jean-Francois Lalonde +1
Adversarial perturbations can deceive neural networks by adding small, imperceptible noise to the input. Recent object trackers with transformer backbones have shown strong perform…
Unifying Graph Convolution and Contrastive Learning in Collaborative Filtering
Yihong Wu, Le Zhang, Fengran Mo +3
Graph-based models and contrastive learning have emerged as prominent methods in Collaborative Filtering (CF). While many existing models in CF incorporate these methods in their d…
Evaluating representation learning on the protein structure universe
Arian R. Jamasb, Alex Morehead, Chaitanya K. Joshi +8
We introduce ProteinWorkshop, a comprehensive benchmark suite for representation learning on protein structures with Geometric Graph Neural Networks. We consider large-scale pre-tr…
Sparse Bayesian Networks: Efficient Uncertainty Quantification in Medical Image Analysis
Zeinab Abboud, Herve Lombaert, Samuel Kadoury
Efficiently quantifying predictive uncertainty in medical images remains a challenge. While Bayesian neural networks (BNN) offer predictive uncertainty, they require substantial co…
Reproducibility Study on Adversarial Attacks Against Robust Transformer Trackers
Fatemeh Nourilenjan Nokabadi, Jean-François Lalonde, Christian Gagné
New transformer networks have been integrated into object tracking pipelines and have demonstrated strong performance on the latest benchmarks. This paper focuses on understanding…
ACCO: Accumulate While You Communicate for Communication-Overlapped Sharded LLM Training
Adel Nabli, Louis Fournier, Pierre Erbacher +3
Training LLMs relies on distributed implementations using multiple GPUs to compute gradients in parallel with sharded optimizers. However, synchronizing gradients in data parallel…
PETRA: Parallel End-to-end Training with Reversible Architectures
Stéphane Rivaud, Louis Fournier, Thomas Pumir +3
Reversible architectures have been shown to be capable of performing on par with their non-reversible architectures, being applied in deep learning for memory savings and generativ…
WASH: Train your Ensemble with Communication-Efficient Weight Shuffling, then Average
Louis Fournier, Adel Nabli, Masih Aminbeidokhti +3
The performance of deep neural networks is enhanced by ensemble methods, which average the output of several models. However, this comes at an increased cost at inference. Weight a…
The Canadian VirusSeq Data Portal & Duotang: open resources for SARS-CoV-2 viral sequences and genomic epidemiology
Erin E. Gill, Baofeng Jia, Carmen Lia Murall +67
The COVID-19 pandemic led to a large global effort to sequence SARS-CoV-2 genomes from patient samples to track viral evolution and inform public health response. Millions of SARS-…