Zobrazeno 1 - 10
of 208 682
pro vyhledávání: '"P STEIN"'
The Stein Variational Gradient Descent method is a variational inference method in statistics that has recently received a lot of attention. The method provides a deterministic approximation of the target distribution, by introducing a nonlocal inter
Externí odkaz:
http://arxiv.org/abs/2412.10295
We propose a Stein variational gradient descent method to concurrently sparsify, train, and provide uncertainty quantification of a complexly parameterized model such as a neural network. It employs a graph reconciliation and condensation process to
Externí odkaz:
http://arxiv.org/abs/2412.16462
We propose a novel method for measuring the discrepancy between a set of samples and a desired posterior distribution for Bayesian inference. Classical methods for assessing sample quality like the effective sample size are not appropriate for scalab
Externí odkaz:
http://arxiv.org/abs/2412.05135
In the present work, we consider existence and multiplicity of positive solutions for nonlocal elliptic problems driven by the Stein-Weiss problem with concave-convex nonlinearities defined in the whole space $\mathbb{R}^N$. More precisely, we consid
Externí odkaz:
http://arxiv.org/abs/2411.06168
Autor:
Sun, Chuhan, Wang, Zipeng
We show that Stein-Weiss inequality of fractional integrals is true for p=q=1. Moreover, by considering a family of strong fractional integral operators whose kernels have singularity on every coordinate subspace, we extend this weighted L^1-result t
Externí odkaz:
http://arxiv.org/abs/2412.19528
Expensive multi-objective optimization problems (EMOPs) are common in real-world scenarios where evaluating objective functions is costly and involves extensive computations or physical experiments. Current Pareto set learning methods for such proble
Externí odkaz:
http://arxiv.org/abs/2412.17312
Autor:
Kusuoka, Seiichiro, Shiozawa, Yuichi
We consider solutions of stochastic differential equations which diverge to infinity as the time parameter goes to infinity. If the coefficients converge as the spacial variable goes to infinity, then the solutions will get close to some Gaussian pro
Externí odkaz:
http://arxiv.org/abs/2411.08725
Deep neural network ensembles are powerful tools for uncertainty quantification, which have recently been re-interpreted from a Bayesian perspective. However, current methods inadequately leverage second-order information of the loss landscape, despi
Externí odkaz:
http://arxiv.org/abs/2411.01887
Stein variational gradient descent (SVGD) [Liu and Wang, 2016] performs approximate Bayesian inference by representing the posterior with a set of particles. However, SVGD suffers from variance collapse, i.e. poor predictions due to underestimating u
Externí odkaz:
http://arxiv.org/abs/2410.22948
Autor:
Benoist, Olivier
We prove that the category of Stein spaces and holomorphic maps is anti-equivalent to the category of Stein algebras and $\mathbb{C}$-algebra morphisms. This removes a finite dimensionality hypothesis from a theorem of Forster.
Comment: 6 pages
Comment: 6 pages
Externí odkaz:
http://arxiv.org/abs/2410.05521