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pro vyhledávání: '"Brouillard, Philippe"'
Autor:
Boussard, Julien, Nagda, Chandni, Kaltenborn, Julia, Lange, Charlotte Emilie Elektra, Brouillard, Philippe, Gurwicz, Yaniv, Nowack, Peer, Rolnick, David
Climate models, such as Earth system models (ESMs), are crucial for simulating future climate change based on projected Shared Socioeconomic Pathways (SSP) greenhouse gas emissions scenarios. While ESMs are sophisticated and invaluable, machine learn
Externí odkaz:
http://arxiv.org/abs/2312.02858
Autor:
Kaltenborn, Julia, Lange, Charlotte E. E., Ramesh, Venkatesh, Brouillard, Philippe, Gurwicz, Yaniv, Nagda, Chandni, Runge, Jakob, Nowack, Peer, Rolnick, David
Climate models have been key for assessing the impact of climate change and simulating future climate scenarios. The machine learning (ML) community has taken an increased interest in supporting climate scientists' efforts on various tasks such as cl
Externí odkaz:
http://arxiv.org/abs/2311.03721
Autor:
Brouillard, Philippe
Dans ce mémoire par articles, nous nous intéressons à l’apprentissage de modèles causaux à partir de données. L’intérêt de cette entreprise est d’obtenir une meilleure compréhension des données et de pouvoir prédire l’effet qu’au
Externí odkaz:
http://hdl.handle.net/1866/25096
Autor:
Brouillard, Philippe, Diallo, El Hadji, Masson, Jean-Bernard, Raymond, Jean-Marc, Riahi, Mounir, Potter, Brian, Kouz, Rémi, Potvin, Jeannot
Publikováno v:
In Canadian Journal of Cardiology July 2024 40(7):1283-1290
Autor:
Brouillard, Philippe, Taslakian, Perouz, Lacoste, Alexandre, Lachapelle, Sebastien, Drouin, Alexandre
Causal discovery from observational data is a challenging task that can only be solved up to a set of equivalent solutions, called an equivalence class. Such classes, which are often large in size, encode uncertainties about the orientation of some e
Externí odkaz:
http://arxiv.org/abs/2107.10703
Autor:
Brouillard, Philippe, Lachapelle, Sébastien, Lacoste, Alexandre, Lacoste-Julien, Simon, Drouin, Alexandre
Learning a causal directed acyclic graph from data is a challenging task that involves solving a combinatorial problem for which the solution is not always identifiable. A new line of work reformulates this problem as a continuous constrained optimiz
Externí odkaz:
http://arxiv.org/abs/2007.01754
We propose a novel score-based approach to learning a directed acyclic graph (DAG) from observational data. We adapt a recently proposed continuous constrained optimization formulation to allow for nonlinear relationships between variables using neur
Externí odkaz:
http://arxiv.org/abs/1906.02226
Autor:
Trofimov, Assya, Brouillard, Philippe, Larouche, Jean-David, Séguin, Jonathan, Laverdure, Jean-Philippe, Brasey, Ann, Ehx, Gregory, Roy, Denis-Claude, Busque, Lambert, Lachance, Silvy, Lemieux, Sébastien, Perreault, Claude
Publikováno v:
In iScience 16 September 2022 25(9)
Akademický článek
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Autor:
Pomey, Marie‐Pascale1,2,3,4 marie-pascale.pomey@umontreal.ca, Brouillard, Philippe5, Ganache, Isabelle1,2, Lambert, Laurie1, Boothroyd, Lucy1, Collette, Caroline1, Bédard, Sylvain3, Grégoire, Alexandre3, Pelaez, Sandra6, Demers‐Payette, Olivier1, Goetghebeur, Mireille1, Guise, Michèle2, Roy, Denis2
Publikováno v:
Health Expectations. Feb2020, Vol. 23 Issue 1, p182-192. 11p. 2 Diagrams, 4 Charts.