Zobrazeno 1 - 10
of 427
pro vyhledávání: '"Castellanos Alejandro"'
Autor:
van der Linden, Putri A., García-Castellanos, Alejandro, Vadgama, Sharvaree, Kuipers, Thijs P., Bekkers, Erik J.
Publikováno v:
Advances in Neural Information Processing Systems (NeurIPS) 2024
Group equivariance has emerged as a valuable inductive bias in deep learning, enhancing generalization, data efficiency, and robustness. Classically, group equivariant methods require the groups of interest to be known beforehand, which may not be re
Externí odkaz:
http://arxiv.org/abs/2412.04594
Relative representations are an established approach to zero-shot model stitching, consisting of a non-trainable transformation of the latent space of a deep neural network. Based on insights of topological and geometric nature, we propose two improv
Externí odkaz:
http://arxiv.org/abs/2409.10967
Autor:
García-Castellanos, Alejandro, Medbouhi, Aniss Aiman, Marchetti, Giovanni Luca, Bekkers, Erik J., Kragic, Danica
We propose HyperSteiner -- an efficient heuristic algorithm for computing Steiner minimal trees in the hyperbolic space. HyperSteiner extends the Euclidean Smith-Lee-Liebman algorithm, which is grounded in a divide-and-conquer approach involving the
Externí odkaz:
http://arxiv.org/abs/2409.05671
Opinion formation by belief propagation: A heuristic to identify low-credible sources of information
With social media, the flow of uncertified information is constantly increasing, with the risk that more people will trust low-credible information sources. To design effective strategies against this phenomenon, it is of paramount importance to unde
Externí odkaz:
http://arxiv.org/abs/2307.03278
Publikováno v:
Cuban Journal of Physics, 1, 2023
Population mobility can be studied readily and cheaply using cellphone data, since people's mobility can be approximately mapped into tower-mobile registries. We model people moving in a grid-like city, where edges of the grid are weighted and paths
Externí odkaz:
http://arxiv.org/abs/2303.00840
Autor:
Sandoval-Rangel, Ladislao, Ramírez-Murillo, Cinthia J., Dimas-Rivera, Gloria L., Rivera De La Rosa, Javier, Lucio-Ortiz, Carlos J., Ahmad, Ejaz, Nigam, K.D.P., Montesinos-Castellanos, Alejandro, Mendoza, Alberto
Publikováno v:
In Industrial Crops & Products 15 September 2024 216
The ancestral sequence reconstruction problem is the inference, back in time, of the properties of common sequence ancestors from measured properties of contemporary populations. Standard algorithms for this problem assume independent (factorized) ev
Externí odkaz:
http://arxiv.org/abs/2108.03801
We adapt the hybrid mechanistic-statistical approach of Ref. [1] to estimate the total number of undocumented Covid-19 infections in Cuba. This scheme is based on the maximum likelihood estimation of a SIR-like model parameters for the infected popul
Externí odkaz:
http://arxiv.org/abs/2008.03332
We apply the cavity master equation (CME) approach to epidemics models. We explore mostly the susceptible-infectious-susceptible (SIS) model, which can be readily treated with the CME as a two-state. We show that this approach is more accurate than i
Externí odkaz:
http://arxiv.org/abs/2006.15881
Autor:
Maillard, Antoine, Foini, Laura, Castellanos, Alejandro Lage, Krzakala, Florent, Mézard, Marc, Zdeborová, Lenka
Publikováno v:
J. Stat. Mech. (2019) 113301
Improved mean-field technics are a central theme of statistical physics methods applied to inference and learning. We revisit here some of these methods using high-temperature expansions for disordered systems initiated by Plefka, Georges and Yedidia
Externí odkaz:
http://arxiv.org/abs/1906.08479