Zobrazeno 1 - 10
of 78
pro vyhledávání: '"Yu, Hanlin"'
Optimization in the Bures-Wasserstein space has been gaining popularity in the machine learning community since it draws connections between variational inference and Wasserstein gradient flows. The variational inference objective function of Kullbac
Externí odkaz:
http://arxiv.org/abs/2410.02490
Autor:
Luu, Hoang Phuc Hau, Yu, Hanlin, Williams, Bernardo, Mikkola, Petrus, Hartmann, Marcelo, Puolamäki, Kai, Klami, Arto
We study a class of optimization problems in the Wasserstein space (the space of probability measures) where the objective function is \emph{nonconvex} along generalized geodesics. When the regularization term is the negative entropy, the optimizatio
Externí odkaz:
http://arxiv.org/abs/2406.00502
Laplace's method approximates a target density with a Gaussian distribution at its mode. It is computationally efficient and asymptotically exact for Bayesian inference due to the Bernstein-von Mises theorem, but for complex targets and finite-data p
Externí odkaz:
http://arxiv.org/abs/2311.02766
Autor:
Zagorowska, Marta, König, Christopher, Yu, Hanlin, Balta, Efe C., Rupenyan, Alisa, Lygeros, John
Optimization-based controller tuning is challenging because it requires formulating optimization problems explicitly as functions of controller parameters. Safe learning algorithms overcome the challenge by creating surrogate models from measured dat
Externí odkaz:
http://arxiv.org/abs/2310.17431
Autor:
Hartmann, Marcelo, Williams, Bernardo, Yu, Hanlin, Girolami, Mark, Barp, Alessandro, Klami, Arto
We consider the fundamental task of optimising a real-valued function defined in a potentially high-dimensional Euclidean space, such as the loss function in many machine-learning tasks or the logarithm of the probability distribution in statistical
Externí odkaz:
http://arxiv.org/abs/2308.08305
Autor:
Yu, Hanlin, Liedienov, N. A., Zatovsky, I. V., Butenko, D. S., Fesych, I. V., Xu, Wei, Rui, Songchun, Li, Quanjun, Liu, Bingbing, Pashchenko, A. V., Levchenko, G. G.
Simultaneous study of magnetic and electrocatalytic properties of cobaltites under extreme conditions expands understanding of physical and chemical processes proceeding in them with the possibility of their further practical application. Therefore,
Externí odkaz:
http://arxiv.org/abs/2303.17148
Stochastic-gradient sampling methods are often used to perform Bayesian inference on neural networks. It has been observed that the methods in which notions of differential geometry are included tend to have better performances, with the Riemannian m
Externí odkaz:
http://arxiv.org/abs/2303.05101
Autor:
Jia, Zekun, Yu, Hanlin, Mo, Hongkun, Song, Yong, Shen, Zhongtao, Zhang, Yunlong, Liu, Jianbei, Peng, Haiping
Publikováno v:
Nuclear Inst. and Methods in Physics Research, A 1050 (2023) 168173
Super Tau-Charm Facility (STCF) is a next-generation high luminosity electron-positron collider facility and is currently one of the major options for accelerator-based particle physics experiment in China. The crystal-based electromagnetic calorimet
Externí odkaz:
http://arxiv.org/abs/2212.09956
Autor:
Cao, Chen, Zhao, Yugang, Zhang, Guiguan, Li, Zhihao, Zhao, Chuang, Yu, Hanlin, Zhao, Dandan, Zhang, Haiyun, Dai, Di
Publikováno v:
In Optics and Laser Technology February 2024 169
Autor:
Song, Yong, Jia, Zekun, Yu, Hanlin, Shen, Zhongtao, Zhang, Yunlong, Liu, Jianbei, Shao, Ming, Peng, Haiping
Publikováno v:
In Nuclear Inst. and Methods in Physics Research, A December 2023 1057