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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 1,200 researchers specializing in machine learning and dedicated to scientific excellence and innovation.

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News

30 Aug 2024
Mila hosted the first Indigenous Pathfinders in AI program
A group picture of the participants of Indigenous Pathfinders in AI program
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29 Aug 2024
Mila and Spin-Ion Technologies announce a partnership to improve neuromorphic algorithms
Mila and Spin Ion logo
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22 Aug 2024
Airudi and Mila Announce Partnership to Propel AI in Human Resources Management
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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

INTREPPPID - An Orthologue-Informed Quintuplet Network for Cross-Species Prediction of Protein-Protein Interaction
Joseph Szymborski
Amin Emad
An overwhelming majority of protein-protein interaction (PPI) studies are conducted in a select few model organisms largely due to constrain… (see more)ts in time and cost of the associated “wet lab” experiments. In silico PPI inference methods are ideal tools to overcome these limitations, but often struggle with cross-species predictions. We present INTREPPPID, a method which incorporates orthology data using a new “quintuplet” neural network, which is constructed with five parallel encoders with shared parameters. INTREPPPID incorporates both a PPI classification task and an orthologous locality task. The latter learns embeddings of orthologues that have small Euclidean distances between them and large distances between embeddings of all other proteins. INTREPPPID outperforms all other leading PPI inference methods tested on both the intra-species and cross-species tasks using strict evaluation datasets. We show that INTREPPPID’s orthologous locality loss increases performance because of the biological relevance of the orthologue data, and not due to some other specious aspect of the architecture. Finally, we introduce PPI.bio and PPI Origami, a web server interface for INTREPPPID and a software tool for creating strict evaluation datasets, respectively. Together, these two initiatives aim to make both the use and development of PPI inference tools more accessible to the community. GRAPHICAL ABSTRACT
2024-08-22
Briefings in Bioinformatics (published)
doi.org
Understanding the Local Geometry of Generative Model Manifolds
Ahmed Imtiaz Humayun
Ibtihel Amara
Candice Schumann
Golnoosh Farnadi
Negar Rostamzadeh
Mohammad Havaei
Deep generative models learn continuous representations of complex data manifolds using a finite number of samples during training. For a pr… (see more)e-trained generative model, the common way to evaluate the quality of the manifold representation learned, is by computing global metrics like Fr\'echet Inception Distance using a large number of generated and real samples. However, generative model performance is not uniform across the learned manifold, e.g., for \textit{foundation models} like Stable Diffusion generation performance can vary significantly based on the conditioning or initial noise vector being denoised. In this paper we study the relationship between the \textit{local geometry of the learned manifold} and downstream generation. Based on the theory of continuous piecewise-linear (CPWL) generators, we use three geometric descriptors - scaling (
2024-08-15
ArXiv (preprint)
arxiv.org
arxiv.org
<scp>RF</scp> shimming in the cervical spinal cord at <scp>7 T</scp>
Daniel Papp
Kyle M. Gilbert
Gaspard Cereza
Alexandre D'Astous
Nibardo Lopez‐Rios
Mathieu Boudreau
Marcus J. Couch
Pedram Yazdanbakhsh
Robert L. Barry
Eva Alonso‐Ortiz
Julien Cohen-Adad
2024-08-13
Magnetic Resonance in Medicine (published)
doi.org
A Survey on Model MoErging: Recycling and Routing Among Specialized Experts for Collaborative Learning
Prateek Yadav
Colin Raffel
Mohammed Muqeeth
Lucas Caccia
Haokun Liu
Tianlong Chen
Mohit Bansal
Leshem Choshen
Alessandro Sordoni
The availability of performant pre-trained models has led to a proliferation of fine-tuned expert models that are specialized to a particula… (see more)r domain or task. Model MoErging methods aim to recycle expert models to create an aggregate system with improved performance or generalization. A key component of MoErging methods is the creation of a router that decides which expert model(s) to use for a particular input or application. The promise, effectiveness, and large design space of MoErging has spurred the development of many new methods over the past few years. This rapid pace of development has made it challenging to compare different MoErging methods, which are rarely compared to one another and are often validated in different experimental setups. To remedy such gaps, we present a comprehensive survey of MoErging methods that includes a novel taxonomy for cataloging key design choices and clarifying suitable applications for each method. Apart from surveying MoErging research, we inventory software tools and applications that make use of MoErging. We additionally discuss related fields of study such as model merging, multitask learning, and mixture-of-experts models. Taken as a whole, our survey provides a unified overview of existing MoErging methods and creates a solid foundation for future work in this burgeoning field.
2024-08-13
ArXiv (preprint)
arxiv.org
arxiv.org
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AI for Humanity

Socially responsible and beneficial development of AI is a fundamental component of Mila’s mission. As a leader in the field, we wish to contribute to social dialogue and the development of applications that will benefit society.

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