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pro vyhledávání: '"Louis, Pascal"'
Autor:
Louis, Pascal
Des difficultés sociales sont liées au trouble de la personnalité limite (TPL). Pour les comprendre, le traitement de l’information lors de décisions sociales doit être étudié. Parmi ces informations sociales, les expressions faciales émoti
Autor:
Zhu, Zhaocheng, Shi, Chence, Zhang, Zuobai, Liu, Shengchao, Xu, Minghao, Yuan, Xinyu, Zhang, Yangtian, Chen, Junkun, Cai, Huiyu, Lu, Jiarui, Ma, Chang, Liu, Runcheng, Xhonneux, Louis-Pascal, Qu, Meng, Tang, Jian
Machine learning has huge potential to revolutionize the field of drug discovery and is attracting increasing attention in recent years. However, lacking domain knowledge (e.g., which tasks to work on), standard benchmarks and data preprocessing pipe
Externí odkaz:
http://arxiv.org/abs/2202.08320
Learning to execute algorithms is a fundamental problem that has been widely studied. Prior work~\cite{veli19neural} has shown that to enable systematic generalisation on graph algorithms it is critical to have access to the intermediate steps of the
Externí odkaz:
http://arxiv.org/abs/2110.14056
Autor:
Facon, Jean-Baptiste, Mainard, Nicolas, Louis, Pascal, Faure, Philippe-Alexandre, Cognet, Jean-Michel
Publikováno v:
In Hand Surgery and Rehabilitation June 2024 43(3)
Link prediction is a very fundamental task on graphs. Inspired by traditional path-based methods, in this paper we propose a general and flexible representation learning framework based on paths for link prediction. Specifically, we define the repres
Externí odkaz:
http://arxiv.org/abs/2106.06935
Autor:
Attal, Nadine, Clairaz-Mahiou, Béatrice, Louis, Pascal, Annenkova, Anna, Milon, Jean-Yves, Bismut, Hervé, Perrot, Serge
Publikováno v:
In La Presse Médicale Open 2024 5
This paper studies learning logic rules for reasoning on knowledge graphs. Logic rules provide interpretable explanations when used for prediction as well as being able to generalize to other tasks, and hence are critical to learn. Existing methods e
Externí odkaz:
http://arxiv.org/abs/2010.04029
This paper studies few-shot relation extraction, which aims at predicting the relation for a pair of entities in a sentence by training with a few labeled examples in each relation. To more effectively generalize to new relations, in this paper we st
Externí odkaz:
http://arxiv.org/abs/2007.02387
This paper builds on the connection between graph neural networks and traditional dynamical systems. We propose continuous graph neural networks (CGNN), which generalise existing graph neural networks with discrete dynamics in that they can be viewed
Externí odkaz:
http://arxiv.org/abs/1912.00967
Publikováno v:
In Orthopaedics & Traumatology: Surgery & Research November 2022 108(7)