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An Empirical Analysis of the Advantages of Finite- v.s. Infinite-Width Bayesian Neural Networks. (arXiv:2211.09184v1 [stat.ML])
Nov. 18, 2022, 2:13 a.m. | Jiayu Yao, Yaniv Yacoby, Beau Coker, Weiwei Pan, Finale Doshi-Velez
stat.ML updates on arXiv.org arxiv.org
Comparing Bayesian neural networks (BNNs) with different widths is
challenging because, as the width increases, multiple model properties change
simultaneously, and, inference in the finite-width case is intractable. In this
work, we empirically compare finite- and infinite-width BNNs, and provide
quantitative and qualitative explanations for their performance difference. We
find that when the model is mis-specified, increasing width can hurt BNN
performance. In these cases, we provide evidence that finite-width BNNs
generalize better partially due to the properties of their …
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