Zobrazeno 1 - 10
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pro vyhledávání: '"raveaux, Romain"'
Autor:
Piquenot, Jason, Bérar, Maxime, Héroux, Pierre, Ramel, Jean-Yves, Raveaux, Romain, Adam, Sébastien
This paper presents Grammar Reinforcement Learning (GRL), a reinforcement learning algorithm that uses Monte Carlo Tree Search (MCTS) and a transformer architecture that models a Pushdown Automaton (PDA) within a context-free grammar (CFG) framework.
Externí odkaz:
http://arxiv.org/abs/2410.01661
Autor:
Piquenot, Jason, Moscatelli, Aldo, Bérar, Maxime, Héroux, Pierre, raveaux, Romain, Ramel, Jean-Yves, Adam, Sébastien
This paper introduces a framework for formally establishing a connection between a portion of an algebraic language and a Graph Neural Network (GNN). The framework leverages Context-Free Grammars (CFG) to organize algebraic operations into generative
Externí odkaz:
http://arxiv.org/abs/2303.01590
Publikováno v:
In Pattern Recognition November 2024 155
Graph matching is an important problem that has received widespread attention, especially in the field of computer vision. Recently, state-of-the-art methods seek to incorporate graph matching with deep learning. However, there is no research to expl
Externí odkaz:
http://arxiv.org/abs/2108.00394
Autor:
Raveaux, Romain
Error-tolerant graph matching gathers an important family of problems. These problems aim at finding correspondences between two graphs while integrating an error model. In the Graph Edit Distance (GED) problem, the insertion/deletion of edges/nodes
Externí odkaz:
http://arxiv.org/abs/2104.06186
Convolutional neural networks (CNNs), in a few decades, have outperformed the existing state of the art methods in classification context. However, in the way they were formalised, CNNs are bound to operate on euclidean spaces. Indeed, convolution is
Externí odkaz:
http://arxiv.org/abs/2002.09285
Autor:
Raveaux, Romain
Les travaux présentés dans ce mémoire de thèse abordent sous différents angles très intéressants, un sujet vaste et ambitieux : l’interprétation de plans cadastraux couleurs.Dans ce contexte, notre approche se trouve à la confluence de dif
Externí odkaz:
http://www.theses.fr/2010LAROS311/document
Graph edit distance (GED) is a powerful and flexible graph matching paradigm that can be used to address different tasks in structural pattern recognition, machine learning, and data mining. In this paper, some new binary linear programming formulati
Externí odkaz:
http://arxiv.org/abs/1505.05740
Publikováno v:
In Computers and Operations Research June 2019 106:225-235
Publikováno v:
In Expert Systems With Applications 15 October 2018 108:183-192