Publications

RECOVER: sequential model optimization platform for combination drug repurposing identifies novel synergistic compounds in vitro
Paul Bertin
Jarrid Rector-Brooks
Deepak Sharma
Thomas Gaudelet
Andrew Anighoro
Torsten Gross
Francisco Martínez-Peña
Eileen L. Tang
S. SurajM
Cristian Regep
Jeremy B.R. Hayter
Maksym Korablyov
N. Valiante
Almer M. van der Sloot
Mike Tyers
Charles E.S. Roberts
Michael M. Bronstein
Luke Lee Lairson
Jake P. Taylor-King
Yoshua Bengio
2022-02-06
ArXiv (preprint)
arxiv.org
arxiv.org
Tackling Climate Change with Machine Learning
David Rolnick
Priya L. Donti
Lynn H. Kaack
Kelly Kochanski
Alexandre Lacoste
Kris Sankaran
Andrew Slavin Ross
Nikola Milojevic-Dupont
Natasha Jaques
Anna Waldman-Brown
Alexandra Luccioni
Tegan Maharaj
Evan D. Sherwin
S. Karthik Mukkavilli
Konrad P. Kording
Carla Gomes
Andrew Y. Ng
Demis Hassabis
John C. Platt
Felix Creutzig … (see 2 more)
Jennifer Chayes
Yoshua Bengio
Climate change is one of the greatest challenges facing humanity, and we, as machine learning experts, may wonder how we can help. Here we d… (see more)escribe how machine learning can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by machine learning, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the machine learning community to join the global effort against climate change.
2022-02-06
ACM Computing Surveys (published)
doi.org
arxiv.org
Exploration with Multi-Sample Target Values for Distributional Reinforcement Learning
Michael Teng
Michiel van de Panne
Frank N. Wood
Distributional reinforcement learning (RL) aims to learn a value-network that predicts the full distribution of the returns for a given stat… (see more)e, often modeled via a quantile-based critic. This approach has been successfully integrated into common RL methods for continuous control, giving rise to algorithms such as Distributional Soft Actor-Critic (DSAC). In this paper, we introduce multi-sample target values (MTV) for distributional RL, as a principled replacement for single-sample target value estimation, as commonly employed in current practice. The improved distributional estimates further lend themselves to UCB-based exploration. These two ideas are combined to yield our distributional RL algorithm, E2DC (Extra Exploration with Distributional Critics). We evaluate our approach on a range of continuous control tasks and demonstrate state-of-the-art model-free performance on difficult tasks such as Humanoid control. We provide further insight into the method via visualization and analysis of the learned distributions and their evolution during training.
2022-02-05
ArXiv (preprint)
arxiv.org
arxiv.org
TIML: Task-Informed Meta-Learning for Agriculture
Gabriel Tseng
Hannah Kerner
David Rolnick
Labeled datasets for agriculture are extremely spatially imbalanced. When developing algorithms for data-sparse regions, a natural approach … (see more)is to use transfer learning from data-rich regions. While standard transfer learning approaches typically leverage only direct inputs and outputs, geospatial imagery and agricultural data are rich in metadata that can inform transfer learning algorithms, such as the spatial coordinates of data-points or the class of task being learned. We build on previous work exploring the use of meta-learning for agricultural contexts in data-sparse regions and introduce task-informed meta-learning (TIML), an augmentation to model-agnostic meta-learning which takes advantage of task-specific metadata. We apply TIML to crop type classification and yield estimation, and find that TIML significantly improves performance compared to a range of benchmarks in both contexts, across a diversity of model architectures. While we focus on tasks from agriculture, TIML could offer benefits to any meta-learning setup with task-specific metadata, such as classification of geo-tagged images and species distribution modelling.
2022-02-03
ArXiv (preprint)
arxiv.org
arxiv.org
Quantum-Inspired Interpretable AI-Empowered Decision Support System for Detection of Early-Stage Rheumatoid Arthritis in Primary Care Using Scarce Dataset
Samira Abbasgholizadeh Rahimi
Mojtaba Kolahdoozi
Arka Mitra
Jose L. Salmeron
Amir Mohammad Navali
Alireza Sadeghpour
Seyed Amir Mir Mohammadi
Rheumatoid arthritis (RA) is a chronic inflammatory and long-term autoimmune disease that can lead to joint and bone erosion. This can lead … (see more)to patients’ disability if not treated in a timely manner. Early detection of RA in settings such as primary care (as the first contact with patients) can have an important role on the timely treatment of the disease. We aim to develop a web-based Decision Support System (DSS) to provide a proper assistance for primary care providers in early detection of RA patients. Using Sparse Fuzzy Cognitive Maps, as well as quantum-learning algorithm, we developed an online web-based DSS to assist in early detection of RA patients, and subsequently classify the disease severity into six different levels. The development process was completed in collaborating with two specialists in orthopedic as well as rheumatology orthopedic surgery. We used a sample of anonymous patient data for development of our model which was collected from Shohada University Hospital, Tabriz, Iran. We compared the results of our model with other machine learning methods (e.g., linear discriminant analysis, Support Vector Machines, and K-Nearest Neighbors). In addition to outperforming other methods of machine learning in terms of accuracy when all of the clinical features are used (accuracy of 69.23%), our model identified the relation of the different features with each other and gave higher explainability comparing to the other methods. For future works, we suggest applying the proposed model in different contexts and comparing the results, as well as assessing its usefulness in clinical practice.
2022-02-02
Mathematics (published)
doi.org
Active Learning for Capturing Human Decision Policies in a Data Frugal Context
Loïc Grossetête
Alexandre Marois
Bénédicte Chatelais
Christian Gagné
Daniel Lafond
2022-02-01
Machine Learning, Optimization, and Data Science (published)
doi.org
Evaluation of a prenatal screening decision aid: A mixed methods pilot study.
Titilayo Tatiana Agbadje
S. A. Rahimi
Mélissa Côté
Andrée-Anne Tremblay
Mariama Penda Diallo
Hélène Elidor
Alex Poulin Herron
Codjo Djignefa Djade
France Légaré
2022-01-31
Patient Education and Counseling (published)
doi.org
How Do Open Source Software Contributors Perceive and Address Usability?: Valued Factors, Practices, and Challenges
Wenting Wang
Jinghui Cheng
Jin L.C. Guo
Given the recent changes in the open source software (OSS) landscape, we examined OSS contributors’ current valued factors, practices, and… (see more) challenges concerning usability. Our survey provides insights for OSS practitioners and tool designers to promote a user-centric mindset and improve usability practice in OSS communities.
2022-01-31
IEEE Software (published)
doi.org
arxiv.org
Improving Sample Efficiency of Value Based Models Using Attention and Vision Transformers
Amir Ardalan Kalantari
Mohammad Saeed Amini
A. Chandar
Doina Precup
Much of recent Deep Reinforcement Learning success is owed to the neural architecture's potential to learn and use effective internal repres… (see more)entations of the world. While many current algorithms access a simulator to train with a large amount of data, in realistic settings, including while playing games that may be played against people, collecting experience can be quite costly. In this paper, we introduce a deep reinforcement learning architecture whose purpose is to increase sample efficiency without sacrificing performance. We design this architecture by incorporating advances achieved in recent years in the field of Natural Language Processing and Computer Vision. Specifically, we propose a visually attentive model that uses transformers to learn a self-attention mechanism on the feature maps of the state representation, while simultaneously optimizing return. We demonstrate empirically that this architecture improves sample complexity for several Atari environments, while also achieving better performance in some of the games.
2022-01-31
ArXiv (preprint)
arxiv.org
arxiv.org
Exploring social inequalities in healthcare trajectories following diagnosis of diabetes: a state sequence analysis of linked survey and administrative data
Rachel McKay
Laurence Letarte
Alain Gillian Lucie David Manon Catherine Anaïs Benoit Alexandre Amélie Pasquale Valérie Marie-Pascale Mike Anne-Marie Marc Josiane Mireille Stéphanie Pierre Annie Isabelle Danielle Denis Jaime André Geneviève Jean-François Roxanne Marc-Antoine Pier Sonia Vanasse
Alain Gillian Lucie David Manon Catherine Anaïs Benoit A Vanasse Bartlett Blais Buckeridge Choinière Hudon
Alain Vanasse
Gillian Bartlett
Lucie Blais
David L Buckeridge
Manon Choinière
Catherine Hudon
Anaïs Lacasse
Benoit Lamarche
Alexandre Lebel
Amélie Quesnel-Vallée
Pasquale Roberge
Valérie Émond
Marie-Pascale Pomey
Mike Benigeri
Anne-Marie Cloutier
Marc Dorais … (see 16 more)
Josiane Courteau
Mireille Courteau
Stéphanie Plante
Pierre Cambon
Annie Giguère
Isabelle Leroux
Danielle St-Laurent
Denis Roy
Jaime Borja
André Néron
Geneviève Landry
Jean-François Ethier
Roxanne Dault
Marc-Antoine Côté-Marcil
Pier Tremblay
Sonia Quirion
2022-01-30
BMC Health Services Research (published)
doi.org
Memory-Aware Functional IR for Higher-Level Synthesis of Accelerators
Christof Schlaak
Tzung-Han Juang
Christophe Dubach
2022-01-30
ACM Transactions on Architecture and Code Optimization (published)
doi.org
Sex-specific lesion pattern of functional outcomes after stroke
Anna K. Bonkhoff
Martin Bretzner
Sungmin Hong
Markus D. Schirmer
Alexander Cohen
Robert W. Regenhardt
Kathleen L. Donahue
Marco J. Nardin
Adrian V. Dalca
Anne-Katrin Giese
Mark R. Etherton
Brandon L. Hancock
Steven J. T. Mocking
Elissa C. McIntosh
John Attia
Oscar R. Benavente
Stephen Bevan
John W. Cole
Amanda Donatti
Christoph J. Griessenauer … (see 39 more)
Laura Heitsch
Lukas Holmegaard
Katarina Jood
Jordi Jimenez-Conde
Steven J. Kittner
Robin Lemmens
Christopher R. Levi
Caitrin W. McDonough
James F. Meschia
Chia-Ling Phuah
Arndt Rolfs
Stefan Ropele
Jonathan Rosand
Jaume Roquer
Tatjana Rundek
Ralph L. Sacco
Reinhold Schmidt
Pankaj Sharma
Agnieszka Slowik
Martin Söderholm
Alessandro Sousa
Tara M. Stanne
Daniel Strbian
Turgut Tatlisumak
Vincent Thijs
Achala Vagal
Johan Wasselius
Daniel Woo
Ramin Zand
Patrick F. McArdle
Bradford B. Worrall
Christina Jern
Arne G. Lindgren
Jane Maguire
Michael D. Fox
Danilo Bzdok
Ona Wu
Natalia S. Rost
Anna K. Martin Sungmin Markus D. Alexander Robert W. Kathleen L. Marco J. Adrian V. Anne-Katrin Mark R. Brandon L. Steven J. T. Elissa C. John Oscar R. Stephen John W. Amanda Christoph J. Laura Lukas Katarina Jordi Steven J. Robin Christopher R. Caitrin W. James F. Chia-Ling Arndt Stefan Jonathan Jaume Tatjana Ralph L. Reinhold Pankaj Agnieszka Martin Alessandro Tara M. Daniel Turgut Vincent Achala Johan Daniel Ramin Patrick F. Bradford B. Christina Arne G. Jane Michael D. Danilo Ona Natalia S. Bonkhoff
Stroke represents a considerable burden of disease for both men and women. However, a growing body of literature suggests clinically relevan… (see more)t sex differences in the underlying causes, presentations and outcomes of acute ischaemic stroke. In a recent study, we reported sex divergences in lesion topographies: specific to women, acute stroke severity was linked to lesions in the left-hemispheric posterior circulation. We here determined whether these sex-specific brain manifestations also affect long-term outcomes. We relied on 822 acute ischaemic patients [age: 64.7 (15.0) years, 39% women] originating from the multi-centre MRI-GENIE study to model unfavourable outcomes (modified Rankin Scale >2) based on acute neuroimaging data in a Bayesian hierarchical framework. Lesions encompassing bilateral subcortical nuclei and left-lateralized regions in proximity to the insula explained outcomes across men and women (area under the curve = 0.81). A pattern of left-hemispheric posterior circulation brain regions, combining left hippocampus, precuneus, fusiform and lingual gyrus, occipital pole and latero-occipital cortex, showed a substantially higher relevance in explaining functional outcomes in women compared to men [mean difference of Bayesian posterior distributions (men – women) = −0.295 (90% highest posterior density interval = −0.556 to −0.068)]. Once validated in prospective studies, our findings may motivate a sex-specific approach to clinical stroke management and hold the promise of enhancing outcomes on a population level.
2022-01-30
Brain Communications (published)
doi.org