Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Dependence between Bayesian neural network units   The connection between Bayesian neural networks and Gaussian processes gained
a lot of attention in the last few years, with the flagship result that hidden
units converge to a Gaussian process limit when the layers width tends to
infinity. Underpinning this result is the fact that hidden units become
independent in the infinite-width limit. Our aim is to shed some light on
hidden units dependence properties in practical finite-width Bayesian neural
networks. In addition to theoretical results, we assess empirically the depth
and width impacts on hidden units dependence properties.

24
The categories of this paper are: dg-ga
34
The categories of this paper are: chem-ph
28
The categories of this paper are: alg-geom
25
The categories of this paper are: patt-sol
10
The categories of this paper are: q-bio
2
The categories of this paper are: math
5
The categories of this paper are: gr-qc
36
The categories of this paper are: cmp-lg
27
The categories of this paper are: comp-gas
23
The categories of this paper are: solv-int
29
The categories of this paper are: funct-an
0
The categories of this paper are: hep-ph
31
The categories of this paper are: ao-sci
9
The categories of this paper are: nlin
33
The categories of this paper are: plasm-ph
4
The categories of this paper are: cond-mat