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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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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

Adaptation, Comparison and Practical Implementation of Fairness Schemes in Kidney Exchange Programs
In Kidney Exchange Programs (KEPs), each participating patient is registered together with an incompatible donor. Donors without an incompat… (see more)ible patient can also register. Then, KEPs typically maximize overall patient benefit through donor exchanges. This aggregation of benefits calls into question potential individual patient disparities in terms of access to transplantation in KEPs. Considering solely this utilitarian objective may become an issue in the case where multiple exchange plans are optimal or near-optimal. In fact, current KEP policies are all-or-nothing, meaning that only one exchange plan is determined. Each patient is either selected or not as part of that unique solution. In this work, we seek instead to find a policy that contemplates the probability of patients of being in a solution. To guide the determination of our policy, we adapt popular fairness schemes to KEPs to balance the usual approach of maximizing the utilitarian objective. Different combinations of fairness and utilitarian objectives are modelled as conic programs with an exponential number of variables. We propose a column generation approach to solve them effectively in practice. Finally, we make an extensive comparison of the different schemes in terms of the balance of utility and fairness score, and validate the scalability of our methodology for benchmark instances from the literature.
Celo: Training Versatile Learned Optimizers on a Compute Diet
Abhinav Moudgil
Boris Knyazev
Learned optimization has emerged as a promising alternative to hand-crafted optimizers, with the potential to discover stronger learned upda… (see more)te rules that enable faster, hyperparameter-free training of neural networks. A critical element for practically useful learned optimizers, that can be used off-the-shelf after meta-training, is strong meta-generalization: the ability to apply the optimizers to new tasks. Recent state-of-the-art work in learned optimizers, VeLO (Metz et al., 2022), requires a large number of highly diverse meta-training tasks along with massive computational resources, 4000 TPU months, to achieve meta-generalization. This makes further improvements to such learned optimizers impractical. In this work, we identify several key elements in learned optimizer architectures and meta-training procedures that can lead to strong meta-generalization. We also propose evaluation metrics to reliably assess quantitative performance of an optimizer at scale on a set of evaluation tasks. Our proposed approach, Celo, makes a significant leap in improving the meta-generalization performance of learned optimizers and also outperforms tuned state-of-the-art optimizers on a diverse set of out-of-distribution tasks, despite being meta-trained for just 24 GPU hours.
Towards fair decentralized benchmarking of healthcare AI algorithms with the Federated Tumor Segmentation (FeTS) challenge
Maximilian Zenk
Ujjwal Baid
Sarthak Pati
Akis Linardos
Brandon Edwards
Micah Sheller
Patrick Foley
Alejandro Aristizabal
David Zimmerer
Alexey Gruzdev
Jason Martin
Russell T. Shinohara
Annika Reinke
Fabian Isensee
Santhosh Parampottupadam
Kaushal Parekh
Ralf Floca
Hasan Kassem
Bhakti Baheti
Siddhesh Thakur … (see 332 more)
Verena Chung
Kaisar Kushibar
Karim Lekadir
Meirui Jiang
Youtan Yin
Hongzheng Yang
Quande Liu
Cheng Chen
Qi Dou
Pheng-Ann Heng
Xiaofan Zhang
Shaoting Zhang
Muhammad Irfan Khan
Mohammad Ayyaz Azeem
Mojtaba Jafaritadi
Esa Alhoniemi
Elina Kontio
Suleiman A. Khan
Leon Mächler
Ivan Ezhov
Florian Kofler
Suprosanna Shit
Johannes C. Paetzold
Timo Loehr
Benedikt Wiestler
Himashi Peiris
Kamlesh Pawar
Shenjun Zhong
Zhaolin Chen
Munawar Hayat
Gary Egan
Mehrtash Harandi
Ece Isik Polat
Gorkem Polat
Altan Kocyigit
Alptekin Temizel
Anup Tuladhar
Lakshay Tyagi
Raissa Souza
Nils D. Forkert
Pauline Mouches
Matthias Wilms
Vishruth Shambhat
Akansh Maurya
Shubham Subhas Danannavar
Rohit Kalla
Vikas Kumar Anand
Ganapathy Krishnamurthi
Sahil Nalawade
Chandan Ganesh
Ben Wagner
Divya Reddy
Yudhajit Das
Fang F. Yu
Baowei Fei
B. Fei
Ananth J. Madhuranthakam
Joseph Maldjian
Gaurav Singh
Jianxun Ren
Wei Zhang
Ning An
Qingyu Hu
Youjia Zhang
Ying Zhou
Vasilis Siomos
Giacomo Tarroni
Jonathan Passerrat-Palmbach
Ambrish Rawat
Giulio Zizzo
Swanand Ravindra Kadhe
Jonathan P. Epperlein
Stefano Braghin
Yuan Wang
Renuga Kanagavelu
Qingsong Wei
Yechao Yang
Yong Liu
Krzysztof Kotowski
Szymon Adamski
Bartosz Machura
Wojciech Malara
Lukasz Zarudzki
Jakub Nalepa
Yaying Shi
Hongjian Gao
Salman Avestimehr
Yonghong Yan
Agus S. Akbar
Ekaterina Kondrateva
Hua Yang
Zhaopei Li
Hung-Yu Wu
Johannes Roth
Camillo Saueressig
Alexandre Milesi
Quoc D. Nguyen
Nathan J. Gruenhagen
Tsung-Ming Huang
Jun Ma
Har Shwinder H. Singh
Nai-Yu Pan
Dingwen Zhang
Ramy A. Zeineldin
Michal Futrega
Yading Yuan
Gian Marco Conte
GM Conte
Xue Feng
Quan D. Pham
Yong Xia
Zhifan Jiang
Huan Minh Luu
Mariia Dobko
Alexandre Carré
Bair Tuchinov
Hassan Mohy-ud-Din
Saruar Alam
Anup Singh
Nameeta Shah
Weichung Wang
Chiharu Sako
Michel Bilello
Satyam Ghodasara
Suyash Mohan
Christos Davatzikos
Evan Calabrese
Jeffrey Rudie
Javier Villanueva-Meyer
S. Cha
Soonmee Cha
Christopher Hess
John Mongan
Madhura Ingalhalikar
Manali Jadhav
Umang Pandey
Jitender Saini
Raymond Y. Huang
Ken Chang
Minh-Son To
Sargam Bhardwaj
Chee Chong
Marc Agzarian
Michal Kozubek
Filip Lux
Jan Michálek
Petr Matula
Miloš Ker^kovský
Tereza Kopr^ivová
Marek Dostál
Václav Vybíhal
Marco C. Pinho
James Holcomb
Marie Metz
Rajan Jain
Matthew D. Lee
Yvonne W. Lui
Pallavi Tiwari
Ruchika Verma
Rohan Bareja
Ipsa Yadav
Jonathan Chen
Neeraj Kumar
Yuriy Gusev
Krithika Bhuvaneshwar
Anousheh Sayah
Camelia Bencheqroun
Anas Belouali
Subha Madhavan
Rivka R. Colen
Aikaterini Kotrotsou
Philipp Vollmuth
Gianluca Brugnara
Chandrakanth J. Preetha
Felix Sahm
Martin Bendszus
Wolfgang Wick
Abhishek Mahajan
Carmen Balaña
Jaume Capellades
Josep Puig
Yoon Seong Choi
Seung-Koo Lee
Jong Hee Chang
Sung Soo Ahn
Hassan F. Shaykh
Alejandro Herrera-Trujillo
Maria Trujillo
William Escobar
Ana Abello
Jose Bernal
Jhon Gómez
Pamela LaMontagne
Daniel S. Marcus
Mikhail Milchenko
Arash Nazeri
BENNETT A. LANDMAN
Karthik Ramadass
Kaiwen Xu
Silky Chotai
Lola B. Chambless
Akshitkumar Mistry
Reid C. Thompson
Ashok Srinivasan
Jayapalli R. Bapuraj
J. Rajiv Bapuraj
Arvind Rao
Nicholas Wang
Ota Yoshiaki
Toshio Moritani
Sevcan Turk
Joonsang Lee
Snehal Prabhudesai
John Garrett
Matthew Larson
Robert Jeraj
Hongwei Li
H. Li
Tobias Weiss
Michael Weller
Andrea Bink
Bertrand Pouymayou
Sonam Sharma
Tzu-Chi Tseng
Saba Adabi
Alexandre Xavier Falcão
Samuel B. Martins
Bernardo C. A. Teixeira
Flávia Sprenger
David Menotti
Diego R. Lucio
Simone P. Niclou
Olivier Keunen
Ann-Christin Hau
Enrique Pelaez
Heydy Franco-Maldonado
Francis Loayza
Sebastian Quevedo
Richard McKinley
Johannes Slotboom
Piotr Radojewski
Raphael Meier
Roland Wiest
Johannes Trenkler
Josef Pichler
Georg Necker
Andreas Haunschmidt
Stephan Meckel
Pamela Guevara
Esteban Torche
Cristobal Mendoza
Franco Vera
Elvis Ríos
Eduardo López
Sergio A. Velastin
Joseph Choi
Stephen Baek
Yusung Kim
Heba Ismael
Bryan Allen
John M. Buatti
Peter Zampakis
Vasileios Panagiotopoulos
Panagiotis Tsiganos
Sotiris Alexiou
Ilias Haliassos
Evangelia I. Zacharaki
Konstantinos Moustakas
Christina Kalogeropoulou
Dimitrios M. Kardamakis
Bing Luo
Laila M. Poisson
Ning Wen
Mahdi A. L. Loutfi
David Fortin
Martin Lepage
Fanny Morón
Jacob Mandel
Gaurav Shukla
Spencer Liem
Gregory S. Alexandre
Joseph Lombardo
Joshua D. Palmer
Adam E. Flanders
Adam P. Dicker
Godwin Ogbole
Dotun Oyekunle
Olubunmi Odafe-Oyibotha
Babatunde Osobu
Mustapha Shu’aibu Hikima
Mayowa Soneye
Farouk Dako
Adeleye Dorcas
Derrick Murcia
Eric Fu
Rourke Haas
John A. Thompson
David Ryan Ormond
Stuart Currie
Kavi Fatania
Russell Frood
Amber L. Simpson
Jacob J. Peoples
Ricky Hu
Danielle Cutler
Fabio Y. Moraes
Anh Tran
Mohammad Hamghalam
Michael A. Boss
James Gimpel
Deepak Kattil Veettil
Kendall Schmidt
Lisa Cimino
Cynthia Price
Brian Bialecki
Sailaja Marella
Charles Apgar
Andras Jakab
Marc-André Weber
Errol Colak
Jens Kleesiek
John Freymann
Justin Kirby
Lena Maier-Hein
Jake Albrecht
Peter Mattson
Alexandros Karargyris
Prashant Shah
Bjoern Menze
Klaus Maier-Hein
Spyridon Bakas
Computational competitions are the standard for benchmarking medical image analysis algorithms, but they typically use small curated test da… (see more)tasets acquired at a few centers, leaving a gap to the reality of diverse multicentric patient data. To this end, the Federated Tumor Segmentation (FeTS) Challenge represents the paradigm for real-world algorithmic performance evaluation. The FeTS challenge is a competition to benchmark (i) federated learning aggregation algorithms and (ii) state-of-the-art segmentation algorithms, across multiple international sites. Weight aggregation and client selection techniques were compared using a multicentric brain tumor dataset in realistic federated learning simulations, yielding benefits for adaptive weight aggregation, and efficiency gains through client sampling. Quantitative performance evaluation of state-of-the-art segmentation algorithms on data distributed internationally across 32 institutions yielded good generalization on average, albeit the worst-case performance revealed data-specific modes of failure. Similar multi-site setups can help validate the real-world utility of healthcare AI algorithms in the future.
Assemblies, synapse clustering, and network topology interact with plasticity to explain structure-function relationships of the cortical connectome
András Ecker
Daniela Egas Santander
Marwan Abdellah
Jorge Blanco Alonso
Sirio Bolaños-Puchet
Giuseppe Chindemi
Dhuruva Priyan Gowri Mariyappan
James B. Isbister
James King
Pramod Kumbhar
Ioannis Magkanaris
Michael W. Reimann
Synaptic plasticity underlies the brain’s ability to learn and adapt. While experiments in brain slices have revealed mechanisms and proto… (see more)cols for the induction of plasticity between pairs of neurons, how these synaptic changes are coordinated in biological neuronal networks to ensure the emergence of learning remains poorly understood. Simulation and modeling have emerged as important tools to study learning in plastic networks, but have yet to achieve a scale that incorporates realistic network structure, active dendrites, and multi-synapse interactions, key determinants of synaptic plasticity. To rise to this challenge, we endowed an existing large-scale cortical network model, incorporating data-constrained dendritic processing and multi-synaptic connections, with a calcium-based model of functional plasticity that captures the diversity of excitatory connections extrapolated to in vivo-like conditions. This allowed us to study how dendrites and network structure interact with plasticity to shape stimulus representations at the microcircuit level. In our exploratory simulations, plasticity acted sparsely and specifically, firing rates and weight distributions remained stable without additional homeostatic mechanisms. At the circuit level, we found plasticity was driven by co-firing stimulus-evoked functional assemblies, spatial clustering of synapses on dendrites, and the topology of the network connectivity. As a result of the plastic changes, the network became more reliable with more stimulus-specific responses. We confirmed our testable predictions in the MICrONS datasets, an openly available electron microscopic reconstruction of a large volume of cortical tissue. Our results quantify at a large scale how the dendritic architecture and higher-order structure of cortical microcircuits play a central role in functional plasticity and provide a foundation for elucidating their role in learning.

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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