Zobrazeno 1 - 10
of 172 235
pro vyhledávání: '"P Louis"'
We present numerical simulations of the impact of laser beam wavefront aberrations in cold atom interferometers. We demonstrate that to reach accuracy at the mrad level, simulations cannot be based on a description of the retroreflection optics only
Externí odkaz:
http://arxiv.org/abs/2410.07720
Autor:
Boudec, Lise Le, de Bezenac, Emmanuel, Serrano, Louis, Regueiro-Espino, Ramon Daniel, Yin, Yuan, Gallinari, Patrick
Physics-informed deep learning often faces optimization challenges due to the complexity of solving partial differential equations (PDEs), which involve exploring large solution spaces, require numerous iterations, and can lead to unstable training.
Externí odkaz:
http://arxiv.org/abs/2410.06820
Autor:
Monteiro, Francisco, Marques, Pedro, Simonini, Alessia, Carbonnelle, Louis, Mendez, Miguel Alfonso
Sloshing of cryogenic liquid propellants can significantly impact a spacecraft's mission safety and performance by unpredictably altering the center of mass and producing large pressure fluctuations due to the increased heat and mass transfer within
Externí odkaz:
http://arxiv.org/abs/2410.06590
A recent line of work in mechanistic interpretability has focused on reverse-engineering the computation performed by neural networks trained on the binary operation of finite groups. We investigate the internals of one-hidden-layer neural networks t
Externí odkaz:
http://arxiv.org/abs/2410.07476
Autor:
Martin-Monier, Louis, Pajovic, Simo, Abebe, Muluneh G., Chen, Joshua, Vaidya, Sachin, Min, Seokhwan, Choi, Seou, Kooi, Steven E., Maes, Bjorn, Hu, Juejun, Soljacic, Marin, Roques-Carmes, Charles
Scintillators are essential for converting X-ray energy into visible light in imaging technologies. Their widespread application in imaging technologies has been enabled by scalable, high-quality, and affordable manufacturing methods. Nanophotonic sc
Externí odkaz:
http://arxiv.org/abs/2410.07141
Autor:
Agrawal, Pravesh, Antoniak, Szymon, Hanna, Emma Bou, Bout, Baptiste, Chaplot, Devendra, Chudnovsky, Jessica, Costa, Diogo, De Monicault, Baudouin, Garg, Saurabh, Gervet, Theophile, Ghosh, Soham, Héliou, Amélie, Jacob, Paul, Jiang, Albert Q., Khandelwal, Kartik, Lacroix, Timothée, Lample, Guillaume, Casas, Diego Las, Lavril, Thibaut, Scao, Teven Le, Lo, Andy, Marshall, William, Martin, Louis, Mensch, Arthur, Muddireddy, Pavankumar, Nemychnikova, Valera, Pellat, Marie, Von Platen, Patrick, Raghuraman, Nikhil, Rozière, Baptiste, Sablayrolles, Alexandre, Saulnier, Lucile, Sauvestre, Romain, Shang, Wendy, Soletskyi, Roman, Stewart, Lawrence, Stock, Pierre, Studnia, Joachim, Subramanian, Sandeep, Vaze, Sagar, Wang, Thomas, Yang, Sophia
We introduce Pixtral-12B, a 12--billion-parameter multimodal language model. Pixtral-12B is trained to understand both natural images and documents, achieving leading performance on various multimodal benchmarks, surpassing a number of larger models.
Externí odkaz:
http://arxiv.org/abs/2410.07073
Autor:
Kirchhof, Michael, Thornton, James, Ablin, Pierre, Béthune, Louis, Ndiaye, Eugene, Cuturi, Marco
The increased adoption of diffusion models in text-to-image generation has triggered concerns on their reliability. Such models are now closely scrutinized under the lens of various metrics, notably calibration, fairness, or compute efficiency. We fo
Externí odkaz:
http://arxiv.org/abs/2410.06025
Homogenization techniques are an appealing approach to reduce computational complexity in systems containing coils with large numbers of high temperature superconductor (HTS) tapes. Resolving all the coated conductor layers and turns in coils is ofte
Externí odkaz:
http://arxiv.org/abs/2410.05121
Autor:
Bogaert, Jeremie, de Marneffe, Marie-Catherine, Descampe, Antonin, Escouflaire, Louis, Fairon, Cedrick, Standaert, Francois-Xavier
Publikováno v:
Traitement Automatique des Langues 64, 2023, ATALA, Paris
Large language models (LLMs) perform very well in several natural language processing tasks but raise explainability challenges. In this paper, we examine the effect of random elements in the training of LLMs on the explainability of their prediction
Externí odkaz:
http://arxiv.org/abs/2410.05085
Autor:
Chaintron, Louis-Pierre
A pathwise large deviation principle in the Wasserstein topology and a pathwise central limit theorem are proved for the empirical measure of a mean-field system of interacting diffusions. The coefficients are path-dependent. The framework allows for
Externí odkaz:
http://arxiv.org/abs/2410.04935