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Inspiring the development of artificial intelligence for the benefit of all 

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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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News

24 Sep 2026
Neurosoft Bioelectronics Partners with Mila to Advance AI-Powered Brain-Computer Interfaces
Logo Mila and Neurosoft
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17 Sep 2026
Mila and Mozilla Announce New Initiative To Build Trustworthy Open Source AI For Everyone
Group photo of Valerie Pisano and CEO of Mozilla
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15 Sep 2026
Canada and Germany Strengthen AI Cooperation Through Strategic Partnership Between DFKI and Mila
Group picture of Valerie Pisano and CEO of DFKI
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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

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 --… (see more) 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 (published)
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 … (see more)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 (published)
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… (see more)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 (published)
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 (published)
doi.org
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Mila Ventures

Mila Ventures

Our venture arm cultivates the next generation of companies backed by Mila's world-class AI research ecosystem. We invest in visionary founders building at the frontier of deep tech, AI, STEM, and beyond.

We believe the future will be shaped by Venture Scientists.

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