Zobrazeno 1 - 10
of 11 391
pro vyhledávání: '"A. Peach"'
Traditional models based solely on pairwise associations often fail to capture the complex statistical structure of multivariate data. Existing approaches for identifying information shared among groups of $d>3$ variables are frequently computational
Externí odkaz:
http://arxiv.org/abs/2408.07533
This study evaluates the performance of ChatGPT variants, GPT-3.5 and GPT-4, both with and without prompt engineering, against solely student work and a mixed category containing both student and GPT-4 contributions in university-level physics coding
Externí odkaz:
http://arxiv.org/abs/2403.16977
Autor:
Sánchez-Lavega, A., del Río-Gaztelurrutia, T., Hueso, R., Pérez-Hoyos, S., García-Melendo, E., Antuñano, A., Mendikoa, I., Rojas, J. F., Lillo, J., Barrado-Navascués, D., Gomez-Forrellad, J. M., Go, C., Peach, D., Barry, T., Milika, D. P., Nicholas, P., Wesley, A., Team, the IOPW-PVOL
Publikováno v:
Geophys. Res. Lett. (2014) 41 1425-1431
We investigate the long-term motion of Saturn's North-Pole Hexagon and the structure of its associated eastward jet, using Cassini ISS and ground-based images from 2008 to 2014. We show that both are persistent features that have survived the long po
Externí odkaz:
http://arxiv.org/abs/2402.06371
Autor:
Peach, Robert L., Vinao-Carl, Matteo, Grossman, Nir, David, Michael, Mallas, Emma, Sharp, David, Malhotra, Paresh A., Vandergheynst, Pierre, Gosztolai, Adam
Gaussian processes (GPs) are popular nonparametric statistical models for learning unknown functions and quantifying the spatiotemporal uncertainty in data. Recent works have extended GPs to model scalar and vector quantities distributed over non-Euc
Externí odkaz:
http://arxiv.org/abs/2309.16746
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-9 (2024)
Abstract This study evaluates the performance of ChatGPT variants, GPT-3.5 and GPT-4, both with and without prompt engineering, against solely student work and a mixed category containing both student and GPT-4 contributions in university-level physi
Externí odkaz:
https://doaj.org/article/449b387b0148408e8b091043c811558c
Models that rely solely on pairwise relationships often fail to capture the complete statistical structure of the complex multivariate data found in diverse domains, such as socio-economic, ecological, or biomedical systems. Non-trivial dependencies
Externí odkaz:
http://arxiv.org/abs/2306.00904
Autor:
Peach, Natalie, Kihas, Ivana, Isik, Ashling, Cassar, Joanne, Barrett, Emma Louise, Cobham, Vanessa, Back, Sudie E., Perrin, Sean, Bendall, Sarah, Brady, Kathleen, Ross, Joanne, Teesson, Maree, Bezzina, Louise, Dobinson, Katherine A., Schollar-Root, Olivia, Milne, Bronwyn, Mills, Katherine L.
Publikováno v:
Advances in Dual Diagnosis, 2024, Vol. 17, Issue 2, pp. 54-71.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/ADD-11-2023-0021
Autor:
Nurisso, Marco, Arnaudon, Alexis, Lucas, Maxime, Peach, Robert L., Expert, Paul, Vaccarino, Francesco, Petri, Giovanni
Simplicial Kuramoto models have emerged as a diverse and intriguing class of models describing oscillators on simplices rather than nodes. In this paper, we present a unified framework to describe different variants of these models, categorized into
Externí odkaz:
http://arxiv.org/abs/2305.17977
Multivariate time series data that capture the temporal evolution of interconnected systems are ubiquitous in diverse areas. Understanding the complex relationships and potential dependencies among co-observed variables is crucial for the accurate st
Externí odkaz:
http://arxiv.org/abs/2305.08529
Autor:
Gosztolai, Adam, Peach, Robert L., Arnaudon, Alexis, Barahona, Mauricio, Vandergheynst, Pierre
The dynamics of neuron populations commonly evolve on low-dimensional manifolds. Thus, we need methods that learn the dynamical processes over neural manifolds to infer interpretable and consistent latent representations. We introduce a representatio
Externí odkaz:
http://arxiv.org/abs/2304.03376