Home

Inspiring the development of artificial intelligence for the benefit of all 

A professor talks to his students in a café/lounge.

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.

About

Featured

News

03 Oct 2024
Bioeureka and Mila join forces to accelerate AI-assisted pathogen identification
logo Mila and Bioeureka
Read the story
01 Oct 2024
Mila Welcomes Material as a New Partner to Drive AI Innovation in Public Company Reporting and Compliance
logo Mila and Material
Read the story
26 Sep 2024
Mila's Yoshua Bengio, Alondra Nelson and many other AI experts, put forward the Manhattan Declaration
Participants at a UN meeting in September 2024
Read the story
See more news

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. 

Browse the online directory

Photo of Yoshua Bengio

Latest Publications

A stochastic integer programming approach to reserve staff scheduling with preferences
Carl Perreault‐Lafleur
Margarida Carvalho
Guy Desaulniers
2025-01-01
Int. Trans. Oper. Res. (published)
doi.org
arxiv.org
A Survey of Diversification Techniques in Search and Recommendation
Haolun Wu
Yansen Zhang
Chen Ma
Fuyuan Lyu
Bowei He
Fernando Diaz
Bhaskar Mitra
Xue (Steve) Liu
Diversifying search results is an important research topic in retrieval systems in order to satisfy both the various interests of customers … (see more)and the equal market exposure of providers. There has been a growing attention on diversity-aware research during recent years, accompanied by a proliferation of literature on methods to promote diversity in search and recommendation. However, the diversity-aware studies in retrieval systems lack a systematic organization and are rather fragmented. In this survey, we are the first to propose a unified taxonomy for classifying the metrics and approaches of diversification in both search and recommendation, which are two of the most extensively researched fields of retrieval systems. We begin the survey with a brief discussion of why diversity is important in retrieval systems, followed by a summary of the various diversity concerns in search and recommendation, highlighting their relationship and differences. For the survey’s main body, we present a unified taxonomy of diversification metrics and approaches in retrieval systems, from both the search and recommendation perspectives. In the later part of the survey, we discuss the openness research questions of diversity-aware research in search and recommendation in an effort to inspire future innovations and encourage the implementation of diversity in real-world systems.
2024-10-01
IEEE Transactions on Knowledge and Data Engineering (published)
doi.org
arxiv.org
Linear Weight Interpolation Leads to Transient Performance Gains
Gaurav Iyer
Gintare Karolina Dziugaite
David Rolnick
2024-09-28
TMLR (accepted)
openreview.net
openreview.net
A neuronal least-action principle for real-time learning in cortical circuits
Walter Senn
Dominik Dold
Akos F. Kungl
Benjamin Ellenberger
Jakob Jordan
Yoshua Bengio
João Sacramento
Mihai A. Petrovici
One of the most fundamental laws of physics is the principle of least action. Motivated by its predictive power, we introduce a neuronal lea… (see more)st-action principle for cortical processing of sensory streams to produce appropriate behavioural outputs in real time. The principle postulates that the voltage dynamics of cortical pyramidal neurons prospectively minimize the local somato-dendritic mismatch error within individual neurons. For motor output neurons, it implies minimizing an instantaneous behavioural error. For deep network neurons, it implies a prospective firing to overcome integration delays and correct for possible output errors right in time. The neuron-specific errors are extracted in the apical dendrites of pyramidal neurons through a cortical microcircuit that tries to explain away the feedback from the periphery, and correct the trajectory on the fly. Any motor output is in a moving equilibrium with the sensory inputs and the motor feedback during the whole sensory-motor trajectory. Ongoing synaptic plasticity reduces the somato-dendritic mismatch error within each cortical neuron and performs gradient descent on the output cost at any moment in time. The neuronal least-action principle offers an axiomatic framework to derive local neuronal and synaptic dynamics for global real-time computation and learning in the brain and in physical substrates in general.
2024-09-23
bioRxiv (preprint)
doi.org
See more publications

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.

Learn more

A person looks up at a starry sky.