Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Huix, Tom"'
In this paper, we rigorously derive Central Limit Theorems (CLT) for Bayesian two-layerneural networks in the infinite-width limit and trained by variational inference on a regression task. The different networks are trained via different maximizatio
Externí odkaz:
http://arxiv.org/abs/2406.09048
Variational inference (VI) is a popular approach in Bayesian inference, that looks for the best approximation of the posterior distribution within a parametric family, minimizing a loss that is typically the (reverse) Kullback-Leibler (KL) divergence
Externí odkaz:
http://arxiv.org/abs/2406.04012
In this paper, we introduce and analyze a variant of the Thompson sampling (TS) algorithm for contextual bandits. At each round, traditional TS requires samples from the current posterior distribution, which is usually intractable. To circumvent this
Externí odkaz:
http://arxiv.org/abs/2307.10167
We provide a rigorous analysis of training by variational inference (VI) of Bayesian neural networks in the two-layer and infinite-width case. We consider a regression problem with a regularized evidence lower bound (ELBO) which is decomposed into th
Externí odkaz:
http://arxiv.org/abs/2307.04779
This paper studies the Variational Inference (VI) used for training Bayesian Neural Networks (BNN) in the overparameterized regime, i.e., when the number of neurons tends to infinity. More specifically, we consider overparameterized two-layer BNN and
Externí odkaz:
http://arxiv.org/abs/2207.03859
We provide a rigorous analysis of training by variational inference (VI) of Bayesian neural networks in the two-layer and infinite-width case. We consider a regression problem with a regularized evidence lower bound (ELBO) which is decomposed into th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::aa706c25b670f02d9a398c2648361315