244 citations
- CentraleSupélecFR42 papers
- Université Paris-SaclayFR24 papers
- Département mathématiques, informatique, sciences de la donnée et technologies du numériqueFR14 papers
- Commissariat à l'Énergie Atomique et aux Énergies AlternativesFR5 papers
- University of PecsHU5 papers
- Institut de Recherche Technologique SystemXFR4 papers
- Laboratoire d'Intégration des Systèmes et des TechnologiesFR4 papers
- Airbus (France)FR3 papers
- DEDUCTEAM: Deduction modulo, interopérabilité et démonstration automatiqueFR3 papers
- Institut Gustave RoussyFR3 papers
- Institut Jean NicodFR3 papers
- Institut National de Recherche pour l'Agriculture, l'Alimentation et l'EnvironnementFR3 papers
Showing 2023 · cs.LGShow all
3 papers · 2 filters
cs.LG2023★ 3 cited
Physics-Informed Graph Convolutional Networks: Towards a generalized framework for complex geometries
Marien Chenaud, José Alves, Frédéric Magoulès
Since the seminal work of [9] and their Physics-Informed neural networks (PINNs), many efforts have been conducted towards solving partial differential equations (PDEs) with Deep L…
cs.LG2023★ 3 cited
Revisiting Instruction Fine-tuned Model Evaluation to Guide Industrial Applications
Manuel Faysse, Gautier Viaud, Céline Hudelot +1
Instruction Fine-Tuning (IFT) is a powerful paradigm that strengthens the zero-shot capabilities of Large Language Models (LLMs), but in doing so induces new evaluation metric requ…
cs.LG2023
An Analysis of Initial Training Strategies for Exemplar-Free Class-Incremental Learning
Grégoire Petit, Michael Soumm, Eva Feillet +4
Class-Incremental Learning (CIL) aims to build classification models from data streams. At each step of the CIL process, new classes must be integrated into the model. Due to catas…