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Located in the heart of Quebec’s AI ecosystem, Mila is a community of more than 1,400 researchers specializing in machine learning and dedicated to scientific excellence and innovation.

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28 Jul 2026
Summer School in Responsible AI and Human Rights: A Fourth Edition That Shines Internationally
Group picture of the Summer School in Responsible AI and Human Rights in Mexico in front of the CINVESTAV building
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23 Jul 2026
Using AI to Modernize Property Inspection
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17 Jul 2026
Disinformation 2.0 in the Age of Artificial Intelligence
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Faculty 

Founded in 1993 by Professor Yoshua Bengio, Mila today brings together over 140 professors affiliated with Université de Montréal, McGill University, Polytechnique Montréal and HEC Montréal. Mila also welcomes professors from Université Laval, Université de Sherbrooke, École de technologie supérieure (ÉTS) and Concordia University. 

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Latest Publications

<scp>1D</scp> Pre‐Acquisition Navigator Correcting Respiratory‐Induced Field Fluctuations in Multi‐Echo Gradient‐Echo Imaging of the Thoracic Spinal Cord
Alicia E. Cronin
Alexandre D’Astous
Nathan Williams
Antoine Guénette
Aimee Salakhov
Seth Stubblefield
Colin D. Mcknight
Lipika Narisetti
Subramaniam Sriram
Seth A. Smith
Ryan K. Robison
Guillaume Gilbert
Julien Cohen‐Adad
Kristin P. O’Grady
PURPOSE: In the spinal cord (SC), multi-echo gradient echo (ME-GRE) increases gray (GM) and white matter (WM) contrast and improves sensitiv… (see more)ity to lesions in people with multiple sclerosis (pwMS). However, SC ME-GRE is susceptible to breathing-induced field fluctuations, causing ghosting artifacts and signal loss. Recent work introduced a 1D phase navigator following the last echo to measure field variations; however, susceptibility to phase wrapping increases at longer echo times. We propose a 1D phase navigator preceding the first echo, reducing phase accumulation and eliminating the need for respiratory monitoring. METHODS: ME-GRE data covering the lower (T9-T12 vertebrae) and upper (T4-T8 vertebrae) thoracic SC were acquired in 20 healthy volunteers and 3 pwMS at 3T. Standard and navigator-corrected images were acquired in the same acquisition. To evaluate image quality, WM and GM signal-to-noise ratio (SNR), WM/GM contrast-to-noise ratio (CNR), and background ghosting signals were measured and compared between the two reconstructions. Both were blindly assessed for artifacts, structural delineation, and diagnostic confidence in pwMS. RESULTS: Navigator correction significantly increased GM and WM SNR and CNR, reduced posterior ghosting across both thoracic regions, and significantly reduced artifacts while increasing structural delineation. Preliminary evaluation in three pwMS showed consistent improvements in artifact mitigation, structural delineation, and lesion conspicuity with navigator correction, providing proof-of-concept for potential clinical application. CONCLUSION: A 1D navigator prior to the first echo reduces ghosting and improves thoracic SC image quality without respiratory monitoring. This approach could improve the diagnostic value and enhance the reliability of thoracic SC ME-GRE.
2026-07-23
Magnetic Resonance in Medicine (published)
doi.org
Controllable and Content-Based Recommendations
Fırat Öncel
Jihoon Jeong
Emiliano Penaloza
Mirco Ravanelli
Laurent Charlin
Cem Subakan
Traditional recommendation systems rely on latent (dense) representations, making them difficult to interpret and control. We propose the Co… (see more)ntrollable and Content-Based Recommendations (CCBR) framework, which builds its recommendations from textual user profile representations. CCBR plugs into collaborative filtering models and introduces controllability via text bottlenecks. We show that CCBR enables text-based and multimodal interventions, allowing users to steer the model towards the directions they prefer. Different from existing controllable recommendation systems, CCBR infers the text summaries directly from item contents (images, audio or video). Across image-, audio-, and video-based datasets, we demonstrate that the proposed framework obtains competitive model performance with standard (latent-representation) models while providing controllable model summaries via text. The model also outperforms TEARS, a recent baseline for controllable recommendation systems. Through systematic interventions, we demonstrate the efficacy of the user steering mechanism.
2026-07-22
arXiv (preprint)
doi.org
arxiv.org
Cortical microstructural integrity predicts an exploitation bias in older adulthood
Patrick Hewan
Alfie Wearn
Jeremy Hogeveen
Kayla Williams
R Nathan Spreng
Gary R. Turner
Sylvia Villeneuve
Judes Poirier
John C S Breitner
Sylvain Baillet
Andrée-Ann Baril
Bellec Pierre
Véronique Bohbot
Danilo Bzdok
Mallar Chakravarty
D Louis Collins
Mahsa Dadar
Simon Ducharme
Alan Evans
Claudine Gauthier … (see 80 more)
Maiya R Geddes
Rick Hoge
Yasser Ituria‐Medina
Gerhard Multhaup
Lisa-Marie Münter
Natasha Rajah
Pedro Rosa-Neto
Taylor Schmitz
Soucy Jp
Nathan Spreng
Christine Tardif
Etienne Vachon-Presseau
Mohammadali Javanray
Meishan Ai
Philippe Amouyel
Nicholas Ashton
Gabriel Aumont‐Rodrigue
Julie Bailly
Guilia Baracchini
Kaj Blennow
Christian Bocti
Lianne Boisvert
Sophie Boutin
Ann Brinkmalm Westman
A P Dagher
Xing Dai
Samir Das
Marina Dauar‐Tedeschi
Louis De Beaumont
Christine Déry
Maxime Descoteaux
Elena Drobotea
M Elie
Alfonso Fajardo Valdez
Vladimir Fonov
David Morgan
Jonathan Gallago
Greco Cr
Louise Hudon
Gabriel Jean
Anne Labonté
Robert Laforce
Marc Lalancette
Jean-Charles Lambert
Jeannie‐Marie Leoutsakos
Danaé Lussier Dumouchel
B Misic
Béry Mohammediyan
Holly NewboldFox
Eugenia Nita Capota
Alix Noly‐Gandon
Adrian Eduardo Noriega de la Colina
Pierre Orban
Valentin Ourry
Cynthia Picard
Alexa Pichet Binette
A. L. Poirier
Nathalie Prenevost
Ting Qiu
Marc James Quesnel
Charles Ramassamy
Jean‐Michel Raoult
Jordana Remz
Safa Sanami
Frederic St‐Onge
Cherie Strikwerda‐Brown
Elisabeth Sylvain
Andràs Tikàsz
Christina Tremblay
Stefanie Tremblay
Jennifer Tremblay‐Mercier
Stéphanie Tullo
Irem Ulku
Paolo Vitali
Yara Yakoub
Robert Zatorre
Henrik Zetterberg
Pierre Bellec
Jean-Paul Soucy
Claudia Greco
OBJECTIVES: Prefrontal regions are implicated in explore-exploit decision-making during foraging. Older adults often show an exploitation bi… (see more)as, and this age period is also marked by deteriorating prefrontal myelination. To investigate whether these phenomena are linked, we examined whether lower magnetization transfer saturation (MTsat), a myelin-sensitive quantitative MRI (qMRI) measure, in these regions predicts greater exploitation bias during foraging, and whether cortical microstructure is a better predictor of bias than macrostructure (i.e., cortical thickness). METHODS: Cognitively healthy older adults with familial risk of Alzheimer's disease (AD) (N=118, 60-88 years) completed a foraging task indexing explore-exploit decision-making. qMRI was used to derive MTsat values for the frontopolar cortex (FPC), medial orbitofrontal cortex (OFC), rostral middle frontal gyrus (rMFG), dorsal anterior cingulate cortex (dACC), as well as the locus coeruleus (LC), a core subcortical region strongly implicated in explore-exploit decision-making. Secondary analyses examined associations between available AD risk markers and foraging. RESULTS: Lower MTsat in the FPC, OFC, rMFG, and LC was associated with an exploitation bias, with LC and FPC emerging as the strongest predictors. No relationship was observed for the dACC. MTsat remained a significant predictor of foraging after controlling for cortical thickness. Observed associations were largely unrelated to AD risk markers. DISCUSSION: Individual differences in cortical microstructural integrity within a well-defined explore-exploit circuit are associated with an exploitative decision-making bias in older adults. These findings highlight the value of qMRI microstructural integrity markers, beyond standard macrostructural assays, in characterizing the neural correlates of exploitation biases in later life.
2026-07-22
Journals of Gerontology Series B: Psychological Sciences and Social Sciences (published)
doi.org
High-resolution dissection of concept acquisition in different families of protein language models
Shawn Whitfield
Tom Marty
Robert M. Vernon
Christopher J. Langmead
Dhanya Sridhar
Quentin Fournier
Protein language models have been increasingly successful on tasks ranging from fitness prediction to functional design, yet what biological… (see more) knowledge they acquire and where it is encoded within their internal representations remain underexplored. Through a high-resolution layer-by-layer interpretability analysis of 8 models from the ESM2 and AMPLIFY families on 22 concepts from human proteome annotations, we found that these models encode concepts of increasing levels of complexity along their depth: basic physicochemical properties and linear motifs are best captured by early-layer embeddings, secondary structure from subsequent layers, and domain-level semantics from middle layers. Principal component projections of these embeddings showed that they separate biologically meaningful protein groupings, and molecular-biology-inspired interventions demonstrated that pLM embeddings can discriminate phosphomimic-active from inactive mutants. Perhaps surprisingly, we observed that pretraining data and compute had a greater impact on the linear emergence of biological concepts than scaling up parameters. By revealing where biological knowledge is captured in pLMs and which choices shape its emergence, our work offers insights to develop more robust, biologically grounded protein language models.
2026-07-22
bioRxiv (accepted)
doi.org
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