no code implementations • JEP/TALN/RECITAL 2021 • Vincent Claveau, Antoine Chaffin, Ewa Kijak
(ii) peuvent-elles remplacer les données d’origines quand ces dernières ne peuvent pas être distribuées, par exemple pour des raisons de confidentialité ?
no code implementations • JEP/TALN/RECITAL 2022 • Antoine Chaffin, Vincent Claveau, Ewa Kijak
Dans cet article, nous explorons comment contrôler la génération de texte au moment du décodage pour satisfaire certaines contraintes (e. g. être non toxique, transmettre certaines émotions...), sans nécessiter de ré-entrainer le modèle de langue.
no code implementations • JEP/TALN/RECITAL 2022 • Antoine Chaffin, Vincent Claveau, Ewa Kijak, Sylvain Lamprier, Benjamin Piwowarski, Thomas Scialom, Jacopo Staiano
Nous évaluons leurs avantages et inconvénients, en explorant leur précision respective sur des tâches de classification, ainsi que leur impact sur la génération coopérative et leur coût de calcul, dans le cadre d’une stratégie de décodage état de l’art, basée sur une recherche arborescente de Monte-Carlo (MCTS).
1 code implementation • 21 Feb 2024 • Antoine Chaffin, Ewa Kijak, Vincent Claveau
Secondly, they can serve as additional trajectories in the RL strategy, resulting in a teacher forcing loss weighted by the similarity of the GT to the image.
no code implementations • 4 Nov 2023 • Mohamed Younes, Ewa Kijak, Richard Kulpa, Simon Malinowski, Franck Multon
In this paper, we propose a novel Multi-Agent Generative Adversarial Imitation Learning based approach that generalizes the idea of motion imitation for one character to deal with both the interaction and the motions of the multiple physics-based characters.
no code implementations • 26 Jun 2023 • Leonardo de Melo Joao, Azael de Melo e Sousa, Bianca Martins dos Santos, Silvio Jamil Ferzoli Guimaraes, Jancarlo Ferreira Gomes, Ewa Kijak, Alexandre Xavier Falcao
State-of-the-art (SOTA) object detection methods have succeeded in several applications at the price of relying on heavyweight neural networks, which makes them inefficient and inviable for many applications with computational resource constraints.
no code implementations • 29 Jun 2022 • Shashanka Venkataramanan, Ewa Kijak, Laurent Amsaleg, Yannis Avrithis
Finally, to address inconsistencies due to linear target interpolation, we introduce a self-distillation approach to generate and interpolate synthetic targets.
1 code implementation • 25 Apr 2022 • Antoine Chaffin, Thomas Scialom, Sylvain Lamprier, Jacopo Staiano, Benjamin Piwowarski, Ewa Kijak, Vincent Claveau
Language models generate texts by successively predicting probability distributions for next tokens given past ones.
no code implementations • 28 Jan 2022 • Sylvain Lamprier, Thomas Scialom, Antoine Chaffin, Vincent Claveau, Ewa Kijak, Jacopo Staiano, Benjamin Piwowarski
Generative Adversarial Networks (GANs) have known a tremendous success for many continuous generation tasks, especially in the field of image generation.
no code implementations • LREC 2022 • Vincent Claveau, Antoine Chaffin, Ewa Kijak
The quality of artificially generated texts has considerably improved with the advent of transformers.
no code implementations • 29 Sep 2021 • Shashanka Venkataramanan, Ewa Kijak, Laurent Amsaleg, Yannis Avrithis
Mixup is a powerful data augmentation method that interpolates between two or more examples in the input or feature space and between the corresponding target labels.
1 code implementation • ICLR 2022 • Shashanka Venkataramanan, Bill Psomas, Ewa Kijak, Laurent Amsaleg, Konstantinos Karantzalos, Yannis Avrithis
In this work, we aim to bridge this gap and improve representations using mixup, which is a powerful data augmentation approach interpolating two or more examples and corresponding target labels at a time.
Ranked #8 on Metric Learning on CUB-200-2011 (using extra training data)
2 code implementations • CVPR 2022 • Shashanka Venkataramanan, Ewa Kijak, Laurent Amsaleg, Yannis Avrithis
Mixup is a powerful data augmentation method that interpolates between two or more examples in the input or feature space and between the corresponding target labels.
Ranked #1 on Representation Learning on CIFAR10
no code implementations • 10 Nov 2020 • Suresh Kirthi Kumaraswamy, Miaojing Shi, Ewa Kijak
Human object interaction (HOI) detection is an important task in image understanding and reasoning.
no code implementations • CVPR 2017 • Ronan Sicre, Yannis Avrithis, Ewa Kijak, Frederic Jurie
This strategy opens the door to the use of PBM in new applications for which the notion of image categories is irrelevant, such as instance-based image retrieval, for example.
no code implementations • COLING 2016 • Vincent Claveau, Ewa Kijak
In this paper, we address the problem of the evaluation of such thesauri or embedding models and compare their results.
no code implementations • JEPTALNRECITAL 2016 • C{\'e}dric Maigrot, Ewa Kijak, Vincent Claveau
Nous pr{\'e}sentons d{'}autre part quelques exp{\'e}riences de d{\'e}tection automatique des messages issus des m{\'e}dias de r{\'e}information, en {\'e}tudiant notamment l{'}influence d{'}attributs de surface et d{'}attributs portant plus sp{\'e}cifiquement sur le contenu de ces messages.
no code implementations • LREC 2016 • Vincent Claveau, Ewa Kijak
In this paper, we address the problem of building and evaluating such thesauri with the help of Information Retrieval (IR) concepts.
no code implementations • JEPTALNRECITAL 2015 • Vincent Claveau, Ewa Kijak
D{'}autre part, nous d{\'e}taillons une m{\'e}thode originale de s{\'e}lection s{'}appuyant sur un crit{\`e}re de respect des proportions dans les jeux de donn{\'e}es manipul{\'e}s. Le bien- fond{\'e} de ces propositions est v{\'e}rifi{\'e} au travers de plusieurs t{\^a}ches et jeux de donn{\'e}es, incluant reconnaissance d{'}entit{\'e}s nomm{\'e}es, chunking, phon{\'e}tisation, d{\'e}sambigu{\"\i}sation de sens.
no code implementations • LREC 2014 • Vincent Claveau, Ewa Kijak
In most Indo-European languages, many biomedical terms are rich morphological structures composed of several constituents mainly originating from Greek or Latin.