Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Tractable Approximate Gaussian Inference for Bayesian Neural Networks   In this paper, we propose an analytical method for performing tractable
approximate Gaussian inference (TAGI) in Bayesian neural networks. The method
enables the analytical Gaussian inference of the posterior mean vector and
diagonal covariance matrix for weights and biases. The method proposed has a
computational complexity of $\mathcal{O}(n)$ with respect to the number of
parameters $n$, and the tests performed on regression and classification
benchmarks confirm that, for a same network architecture, it matches the
performance of existing methods relying on gradient backpropagation.

9
The categories of this paper are: nlin
19
The categories of this paper are: eess
6
The categories of this paper are: astro-ph
31
The categories of this paper are: ao-sci
18
The categories of this paper are: econ
7
The categories of this paper are: hep-th
22
The categories of this paper are: chao-dyn
35
The categories of this paper are: mtrl-th
3
The categories of this paper are: physics
5
The categories of this paper are: gr-qc
2
The categories of this paper are: math
13
The categories of this paper are: quant-ph
30
The categories of this paper are: q-alg
11
The categories of this paper are: hep-lat
37
The categories of this paper are: supr-con
26
The categories of this paper are: bayes-an