Zobrazeno 1 - 10
of 5 180
pro vyhledávání: '"A. Dedieu"'
Autor:
Zhou, Guangyao, Swaminathan, Sivaramakrishnan, Raju, Rajkumar Vasudeva, Guntupalli, J. Swaroop, Lehrach, Wolfgang, Ortiz, Joseph, Dedieu, Antoine, Lázaro-Gredilla, Miguel, Murphy, Kevin
We propose Diffusion Model Predictive Control (D-MPC), a novel MPC approach that learns a multi-step action proposal and a multi-step dynamics model, both using diffusion models, and combines them for use in online MPC. On the popular D4RL benchmark,
Externí odkaz:
http://arxiv.org/abs/2410.05364
Autor:
Ortiz, Joseph, Dedieu, Antoine, Lehrach, Wolfgang, Guntupalli, Swaroop, Wendelken, Carter, Humayun, Ahmad, Zhou, Guangyao, Swaminathan, Sivaramakrishnan, Lázaro-Gredilla, Miguel, Murphy, Kevin
Learning from previously collected data via behavioral cloning or offline reinforcement learning (RL) is a powerful recipe for scaling generalist agents by avoiding the need for expensive online learning. Despite strong generalization in some respect
Externí odkaz:
http://arxiv.org/abs/2409.18330
Autor:
Dedieu, Lucas, Nerrienet, Nicolas, Nivaggioli, Adrien, Simmat, Clara, Clavel, Marceau, Gauthier, Arnaud, Sockeel, Stéphane, Peyret, Rémy
Recent advancements in deep learning have proven highly effective in medical image classification, notably within histopathology. However, noisy labels represent a critical challenge in histopathology image classification, where accurate annotations
Externí odkaz:
http://arxiv.org/abs/2404.07605
Despite their stellar performance on a wide range of tasks, including in-context tasks only revealed during inference, vanilla transformers and variants trained for next-token predictions (a) do not learn an explicit world model of their environment
Externí odkaz:
http://arxiv.org/abs/2401.05946
Autor:
V. Forcadell, C. Augros, O. Caumont, K. Dedieu, M. Ouradou, C. David, J. Figueras i Ventura, O. Laurantin, H. Al-Sakka
Publikováno v:
Atmospheric Measurement Techniques, Vol 17, Pp 6707-6734 (2024)
Radar has consistently been proven to be the most reliable source of information for the remote detection of hail within storms in real time. Currently, existing hail detection techniques have limited ability to clearly distinguish storms that produc
Externí odkaz:
https://doaj.org/article/43d87277758747948f258d627b16c652
Autor:
Swaminathan, Sivaramakrishnan, Dedieu, Antoine, Raju, Rajkumar Vasudeva, Shanahan, Murray, Lazaro-Gredilla, Miguel, George, Dileep
In-context learning (ICL) is one of the most powerful and most unexpected capabilities to emerge in recent transformer-based large language models (LLMs). Yet the mechanisms that underlie it are poorly understood. In this paper, we demonstrate that c
Externí odkaz:
http://arxiv.org/abs/2307.01201
Autor:
Ciliberto, Ciro, Dedieu, Thomas
Publikováno v:
Ãpijournal de Géométrie Algébrique, Special volume in honour of Claire Voisin (July 9, 2024) epiga:11202
We classify linearly normal surfaces $S \subset \mathbf{P}^{r+1}$ of degree $d$ such that $4g-4 \leq d \leq 4g+4$, where $g>1$ is the sectional genus (it is a classical result that for larger $d$ there are only cones). We apply this to the study of t
Externí odkaz:
http://arxiv.org/abs/2304.01851
Autor:
Aurélie Moniot, Christophe Schneider, Laure Chardin, Elisa Yaniz-Galende, Catherine Genestie, Marion Etiennot, Aubéri Henry, Coralie Drelon, Audrey Le Formal, Benoit Langlois, Laurence Venat, Christophe Louvet, Laure Favier, Alain Lortholary, Dominique Berton-Rigaud, Nadine Dohollou, Christophe Desauw, Michel Fabbro, Emmanuelle Malaurie, Coraline Dubot, Jean Emmanuel Kurtz, Nathalie Bonichon Lamichhane, Éric Pujade-Lauraine, Albin Jeanne, Alexandra Leary, Stéphane Dedieu
Publikováno v:
Molecular Cancer, Vol 23, Iss 1, Pp 1-17 (2024)
Abstract Background Ovarian cancer (OC) remains one of the most challenging and deadly malignancies facing women today. While PARP inhibitors (PARPis) have transformed the treatment landscape for women with advanced OC, many patients will relapse and
Externí odkaz:
https://doaj.org/article/dd3b485e8ca3415e9aedea04cb9e8214
Noisy-OR Bayesian Networks (BNs) are a family of probabilistic graphical models which express rich statistical dependencies in binary data. Variational inference (VI) has been the main method proposed to learn noisy-OR BNs with complex latent structu
Externí odkaz:
http://arxiv.org/abs/2302.00099
Publikováno v:
Data in Brief, Vol 57, Iss , Pp 110907- (2024)
The current agroecological transition of agriculture pushes to a diversification of cropping systems, which requires quantified data describing crop successions. “Crop successions indicators 2015-2021” dataset provides a set of twenty synthesis i
Externí odkaz:
https://doaj.org/article/01defe9b4b3f4db9bbdea93333396043