Zobrazeno 1 - 10
of 31 741
pro vyhledávání: '"Lobato AS"'
Autor:
Pérez de Llano LA, Cosío BG, Lobato Astiárraga I, Soto Campos G, Tejedor Alonso MA, Marina Malanda N, Padilla Galo A, Urrutia Landa I, Michel de la Rosa FJ, García-Moguel I
Publikováno v:
Journal of Asthma and Allergy, Vol Volume 15, Pp 79-88 (2022)
Luis A Pérez de Llano,1,* Borja G Cosío,2,* Ignacio Lobato Astiárraga,3 Gregorio Soto Campos,4 Miguel Ángel Tejedor Alonso,5 Nuria Marina Malanda,6 Alicia Padilla Galo,7 Isabel Urrutia Landa,8 Francisco J Michel de la Rosa,9 Ismael Garcí
Externí odkaz:
https://doaj.org/article/f95bd090a9d84008b6029069ca82c387
Autor:
Shysheya, Aliaksandra, Diaconu, Cristiana, Bergamin, Federico, Perdikaris, Paris, Hernández-Lobato, José Miguel, Turner, Richard E., Mathieu, Emile
Modelling partial differential equations (PDEs) is of crucial importance in science and engineering, and it includes tasks ranging from forecasting to inverse problems, such as data assimilation. However, most previous numerical and machine learning
Externí odkaz:
http://arxiv.org/abs/2410.16415
Training generative models to sample from unnormalized density functions is an important and challenging task in machine learning. Traditional training methods often rely on the reverse Kullback-Leibler (KL) divergence due to its tractability. Howeve
Externí odkaz:
http://arxiv.org/abs/2410.12456
Autor:
Fromer, Jenna, Wang, Runzhong, Manjrekar, Mrunali, Tripp, Austin, Hernández-Lobato, José Miguel, Coley, Connor W.
Batched Bayesian optimization (BO) can accelerate molecular design by efficiently identifying top-performing compounds from a large chemical library. Existing acquisition strategies for batch design in BO aim to balance exploration and exploitation.
Externí odkaz:
http://arxiv.org/abs/2410.06333
Current methods for compressing neural network weights, such as decomposition, pruning, quantization, and channel simulation, often overlook the inherent symmetries within these networks and thus waste bits on encoding redundant information. In this
Externí odkaz:
http://arxiv.org/abs/2410.01309
Autor:
Sabanza-Gil, Víctor, Barbano, Riccardo, Gutiérrez, Daniel Pacheco, Luterbacher, Jeremy S., Hernández-Lobato, José Miguel, Schwaller, Philippe, Roch, Loïc
Multi-fidelity Bayesian Optimization (MFBO) is a promising framework to speed up materials and molecular discovery as sources of information of different accuracies are at hand at increasing cost. Despite its potential use in chemical tasks, there is
Externí odkaz:
http://arxiv.org/abs/2410.00544
Autor:
Bagley, Nicholas, Wehbi, Sahar, Mansuryan, Tigran, Boulesteix, Rémy, Maître, Alexandre, Lobato, Yago Arosa, Ferraro, Mario, Mangini, Fabio, Sun, Yifan, Krupa, Katarzyna, Wetzel, Benjamin, Couderc, Vincent, Wabnitz, Stefan, Aceves, Alejandro, Tonello, Alessandro
A coherent concatenation of multiple Townes solitons may lead to a stable infrared and visible broadband filament in ceramic YAG polycrystal. This self-trapped soliton train helps implement self-referenced multiplex coherent anti-Stokes Raman scatter
Externí odkaz:
http://arxiv.org/abs/2409.17040
Developing an efficient sampler capable of generating independent and identically distributed (IID) samples from a Boltzmann distribution is a crucial challenge in scientific research, e.g. molecular dynamics. In this work, we intend to learn neural
Externí odkaz:
http://arxiv.org/abs/2409.09787
Autor:
Zhang, Fengzhe, He, Jiajun, Midgley, Laurence I., Antorán, Javier, Hernández-Lobato, José Miguel
Diffusion models have shown promising potential for advancing Boltzmann Generators. However, two critical challenges persist: (1) inherent errors in samples due to model imperfections, and (2) the requirement of hundreds of functional evaluations (NF
Externí odkaz:
http://arxiv.org/abs/2409.07323
Autor:
Bergna, Richard, Calvo-Ordoñez, Sergio, Opolka, Felix L., Liò, Pietro, Hernandez-Lobato, Jose Miguel
We address the problem of learning uncertainty-aware representations for graph-structured data. While Graph Neural Ordinary Differential Equations (GNODE) are effective in learning node representations, they fail to quantify uncertainty. To address t
Externí odkaz:
http://arxiv.org/abs/2408.16115