Zobrazeno 1 - 10
of 3 205
pro vyhledávání: '"A. Raventos"'
Point processes model the distribution of random point sets in mathematical spaces, such as spatial and temporal domains, with applications in fields like seismology, neuroscience, and economics. Existing statistical and machine learning models for p
Externí odkaz:
http://arxiv.org/abs/2410.22493
Get rich quick: exact solutions reveal how unbalanced initializations promote rapid feature learning
Autor:
Kunin, Daniel, Raventós, Allan, Dominé, Clémentine, Chen, Feng, Klindt, David, Saxe, Andrew, Ganguli, Surya
While the impressive performance of modern neural networks is often attributed to their capacity to efficiently extract task-relevant features from data, the mechanisms underlying this rich feature learning regime remain elusive, with much of our the
Externí odkaz:
http://arxiv.org/abs/2406.06158
To the best of our knowledge, a complete characterization of the domains that escape the famous Arrow's impossibility theorem remains an open question. We believe that different ways of proving Arrovian theorems illuminate this problem. This paper pr
Externí odkaz:
http://arxiv.org/abs/2402.06024
In the present paper we study necessary and sufficient conditions for the existence of a semicontinuous and finite Richter-Peleg multi-utility for a preorder. It is well know that, given a preorder on a topological space, if there is a lower (upper)
Externí odkaz:
http://arxiv.org/abs/2401.13392
Pretrained transformers exhibit the remarkable ability of in-context learning (ICL): they can learn tasks from just a few examples provided in the prompt without updating any weights. This raises a foundational question: can ICL solve fundamentally $
Externí odkaz:
http://arxiv.org/abs/2306.15063
In this article, working in the spirit of the classical Arrovian models in the fuzzy setting and their possible extensions, we go deeper into the study of some type of decompositions defined by t-norms and t-conorms. This allows us to achieve charact
Externí odkaz:
http://arxiv.org/abs/2306.01165
Autor:
M. Di Stefano, D. Nerini, I. Alvarez, G. Ardizzone, P. Astruch, G. Basterretxea, A. Blanfuné, D. Bonhomme, A. Calò, I. Catalan, C. Cattano, A. Cheminée, R. Crec'hriou, A. Cuadros, A. Di Franco, C. Diaz-Gil, T. Estaque, R. Faillettaz, F. C. Félix-Hackradt, J. A. Garcia-Charton, P. Guidetti, L. Guilloux, J.-G. Harmelin, M. Harmelin-Vivien, M. Hidalgo, H. Hinz, J.-O. Irisson, G. La Mesa, L. Le Diréach, P. Lenfant, E. Macpherson, S. Matić-Skoko, M. Mercader, M. Milazzo, T. Monfort, J. Moranta, M. Muntoni, M. Murenu, L. Nunez, M. P. Olivar, J. Pastor, Á. Pérez-Ruzafa, S. Planes, N. Raventos, J. Richaume, E. Rouanet, E. Roussel, S. Ruitton, A. Sabatés, T. Thibaut, D. Ventura, L. Vigliola, D. Vrdoljak, V. Rossi
Publikováno v:
Earth System Science Data, Vol 16, Pp 3851-3871 (2024)
Early-life stages play a key role in the dynamics of bipartite life cycle marine fish populations. Difficult to monitor, observations of these stages are often scattered in space and time. While Mediterranean coastlines have often been surveyed, no e
Externí odkaz:
https://doaj.org/article/729921f3bfee419987caed2021980a2b
Sequential Monte Carlo (SMC) is an inference algorithm for state space models that approximates the posterior by sampling from a sequence of target distributions. The target distributions are often chosen to be the filtering distributions, but these
Externí odkaz:
http://arxiv.org/abs/2206.05952
Autor:
Ferreira, Ana Sofia, Silva, Ana Margarida, Laveriano-Santos, Emily P., Lozano-Castellón, Julián, Lamuela-Raventós, Rosa M., Švarc-Gajíc, Jaroslava, Delerue-Matos, Cristina, Estevinho, Berta N., Costa, Paulo C., Rodrigues, Francisca
Publikováno v:
In Powder Technology 1 October 2024 446
Autor:
Ivana K. Levy, Débora Salustro, Fernando Battaglini, Leonardo Lizarraga, Daniel H. Murgida, Rosalía Agusti, Norma D’Accorso, Dorotea Raventos Segura, Lorena González Palmén, R. Martín Negri
Publikováno v:
ACS Omega, Vol 9, Iss 9, Pp 10445-10458 (2024)
Externí odkaz:
https://doaj.org/article/8b5dabf9545c491099974e826372cb19