Zobrazeno 1 - 10
of 20 401
pro vyhledávání: '"Pierre Louis"'
Autor:
Hamonic, Pierre, Nurizzo, Martin, Nath, Jayshankar, Dartiailh, Matthieu C., El-Homsy, Victor, Fragnol, Mathis, Martinez, Biel, Julliard, Pierre-Louis, Paz, Bruna Cardoso, Ouvrier-Buffet, Mathilde, Filippini, Jean-Baptiste, Bertrand, Benoit, Niebojewski, Heimanu, Bäuerle, Christopher, Vinet, Maud, Balestro, Franck, Meunier, Tristan, Urdampilleta, Matias
Semiconductor quantum dot arrays are a promising platform to perform spin-based error-corrected quantum computation with large numbers of qubits. However, due to the diverging number of possible charge configurations combined with the limited sensiti
Externí odkaz:
http://arxiv.org/abs/2410.02325
Autor:
Kieokaew, Rungployphan, Haberle, Veronika, Marchaudon, Aurélie, Blelly, Pierre-Louis, Chambodut, Aude
Geomagnetic indices derived from ground magnetic measurements characterize the intensity of solar-terrestrial interaction. The \textit{Kp} index derived from multiple magnetic observatories at mid-latitude has commonly been used for space weather ope
Externí odkaz:
http://arxiv.org/abs/2410.02311
An approximation of the squared Wasserstein distance and an application to Hamilton-Jacobi equations
Autor:
Bertucci, Charles, Lions, Pierre Louis
We provide a simple $C^{1,1}$ approximation of the squared Wasserstein distance on R^d when one of the two measures is fixed. This approximation converges locally uniformly. More importantly, at points where the differential of the squared Wasserstei
Externí odkaz:
http://arxiv.org/abs/2409.11793
Autor:
Wei, Jialiang, Courbis, Anne-Lise, Lambolais, Thomas, Xu, Binbin, Bernard, Pierre Louis, Dray, Gérard, Maalej, Walid
Over the past decade, app store (AppStore)-inspired requirements elicitation has proven to be highly beneficial. Developers often explore competitors' apps to gather inspiration for new features. With the advance of Generative AI, recent studies have
Externí odkaz:
http://arxiv.org/abs/2408.17404
Autor:
Benoit Mauvieux, Corentin Hingrand, Joffrey Drigny, Amir Hodzic, Pauline Baron, Rémy Hurdiel, Romain Jouffroy, Jean-Charles Vauthier, Mathias Pessiglione, Antonius Wiehler, Francis Degache, Sébastien Pavailler, Elsa Heyman, Mathilde Plard, Philippe Noirez, Blaise Dubois, Jean François Esculier, Anh Phong Nguyen, Joachim Van Cant, Olivier Roy Baillargeon, Benoit Pairot de Fontenay, Pierre Louis Delaunay, Stéphane Besnard
Publikováno v:
JMIR Research Protocols, Vol 11, Iss 6, p e38027 (2022)
BackgroundThe growing interest of the scientific community in trail running has highlighted the acute effects of practice at the time of these races on isolated aspects of physiological and structural systems; biological, physiological, cognitive, an
Externí odkaz:
https://doaj.org/article/e1857aa0a43344b2844745ed7558acd8
Autor:
Lanzoni, Daniele, Fantasia, Andrea, Bergamaschini, Roberto, Pierre-Louis, Olivier, Montalenti, Francesco
A Convolutional Recurrent Neural Network (CRNN) is trained to reproduce the evolution of the spinodal decomposition process in three dimensions as described by the Cahn-Hilliard equation. A specialized, physics-inspired architecture is proven to prov
Externí odkaz:
http://arxiv.org/abs/2407.20126
The Hitchin component of the character variety of representations of a surface group $\pi_1(S)$ into $\mathrm{PSL}_d(\mathbb R)$ for some $d\geq 3$ can be equipped with a pressure metric whose restriction to the Fuchsian locus equals the Weil-Peterss
Externí odkaz:
http://arxiv.org/abs/2407.07748
Autor:
Ramé, Alexandre, Ferret, Johan, Vieillard, Nino, Dadashi, Robert, Hussenot, Léonard, Cedoz, Pierre-Louis, Sessa, Pier Giuseppe, Girgin, Sertan, Douillard, Arthur, Bachem, Olivier
Reinforcement learning from human feedback (RLHF) aligns large language models (LLMs) by encouraging their generations to have high rewards, using a reward model trained on human preferences. To prevent the forgetting of pre-trained knowledge, RLHF u
Externí odkaz:
http://arxiv.org/abs/2406.16768
We propose the Random Subspace Homogenized Trust Region (RSHTR) method, which efficiently solves high-dimensional non-convex optimization problems by identifying descent directions within randomly selected subspaces. RSHTR provides the strongest theo
Externí odkaz:
http://arxiv.org/abs/2406.14337