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Inspirer le développement de l'intelligence artificielle au bénéfice de tous·tes

Un professeur s'entretient avec ses étudiants dans un café/lounge.

Situé au cœur de l’écosystème québécois en intelligence artificielle (IA), Mila rassemble une communauté de plus de 1400 personnes spécialisées en apprentissage automatique et dédiées à l’excellence scientifique et l’innovation.

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Nouvelles

24 Sep 2026
Neurosoft Bioelectronics s'associe à Mila pour faire progresser les interfaces cerveau-machine basées sur l'IA
Logo Mila et Neurosoft
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17 Sep 2026
Mila et Mozilla annoncent une nouvelle initiative pour bâtir une IA ouverte digne de confiance pour tous·tes
Photo de groupe de Valerie Pisano et dirigeant de Mozilla
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15 Sep 2026
Le Canada et l'Allemagne renforcent leur coopération en IA grâce à un partenariat stratégique entre le DFKI et Mila
Photo de groupe de Valerie Pisano et dirigeant de DFKI
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Corps professoral

Fondé en 1993 par le professeur Yoshua Bengio, Mila regroupe aujourd'hui plus de 140 professeur·e·s affilié·e·s à l'Université de Montréal, l'Université McGill, Polytechnique Montréal et HEC Montréal. L'institut accueille également des professeur·e·s de l'Université Laval, de l'Université de Sherbrooke, de l'École de technologie supérieure (ÉTS) et de l'Université Concordia.

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Photo de Yoshua Bengio

Publications récentes

ECUAS: A family of metrics for principled evaluation of uncertainty-augmented systems
Lautaro Estienne
Erik Ernst
Matias Vera
Pablo Piantanida
LUCIANA FERRER
In high-stakes automated decision-making, access to predictive uncertainty is essential for enabling users -- human or downstream systems --… (voir plus) to accept or reject predictions based on application-specific cost trade-offs. Such uncertainty-augmented (UA) systems -- i.e., systems that output both predictions and uncertainty scores -- are currently being assessed in the literature in a variety of ways, using separate metrics to evaluate the predictions and the uncertainty scores, setting a cost function with a fixed rejection cost or integrating over a coverage-risk curve. We argue that these evaluation approaches are inadequate for assessing overall performance of the UA system for decision making under uncertainty and propose a novel family of metrics, ECUAS
2026-12-30
OpenReview (publié)
openreview.net
openreview.net
Generative Cutout Animation
Ivan Puhachov
Noam Aigerman
Thibault Groueix
Mikhail Bessmeltsev
Abstract Cutout animation is one of the earliest forms of animation, and to this day remains a popular technique featured in numerous films … (voir plus)including Monty Python and South Park series. Most computer animation systems, however, focus on different styles, including cel animation, making cutout animation somewhat underexplored. As creating cutouts is meticulous, we propose a novel generative cutout animation system. Taking a skeletal animation and a text prompt as input, we automatically generate a 2.5D cutout rig ready for production in films and games. Our system optimizes cutout images with an SDS (Score Distillation Sampling) loss with a LoRA (Low‐Rank Adaptation) prior, in multiple target poses. Naïvely optimizing an SDS loss, however, would lead to inconsistent target pose images, and, as a result, blurry or transparent cutouts. To address this, we introduce a novel optimization with techniques targeting pose and noise consistency, resulting in coherent target images and sharp cutouts. We validate our system by demonstrating a gallery of results, comparing with previous works, ablations, and other analyses. Once generated, our cutout rigs can be used both for the given input animation and repurposed for other animations or edited as independent assets.
2026-12-30
OpenReview (publié)
doi.org
openreview.net
QueStER: Query Specification for Generative Keyword-Based Retrieval
Arthur SATOUF
Yuxuan Zong
Habiboulaye Amadou Boubacar
Pablo Piantanida
Benjamin Piwowarski
Generative retrieval (GR) differs from the traditional index–then–retrieve pipeline by storing relevance in model parameters and generat… (voir plus)ing retrieval cues directly from the query, but it can be brittle out of domain and expensive to scale. We introduce QueStER (QUEry SpecificaTion for gEnerative Keyword-Based Retrieval), which bridges GR and query reformulation by learning to generate explicit keyword-based search specifications. Given a user query, a lightweight LLM produces a keyword query that is executed by a standard retriever (BM25), combining the generalization benefits of generative query rewriting with the efficiency and scalability of lexical indexing. We train the rewriting policy with reinforcement learning techniques. Across in- and out-of-domain evaluations, QueStER consistently improves over BM25 and is competitive with neural IR baselines, while maintaining strong efficiency.
2026-12-30
OpenReview (publié)
openreview.net
openreview.net
Examining the relationship between secondary students’ ICT engagement and computational thinking achievement: Does gender make a difference?
Echo Zexuan Pan
Matthew D. Johnson
Maria Cutumisu
2026-11-30
Studies in Educational Evaluation (publié)
doi.org
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Mila Ventures

Mila Ventures

Notre branche de capital de risque cultive la prochaine génération d'entreprises, soutenues par l'écosystème de recherche en IA de classe mondiale de Mila. Nous investissons dans des fondateurs·rices visionnaires qui bâtissent à la frontière des technologies de pointe, de l'IA, des STIM et au-delà.

Nous croyons que l'avenir sera façonné par les entrepreneur·e·s scientifiques.

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