Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Sampling-Free Variational Inference of Bayesian Neural Networks by
  Variance Backpropagation   We propose a new Bayesian Neural Net formulation that affords variational
inference for which the evidence lower bound is analytically tractable subject
to a tight approximation. We achieve this tractability by (i) decomposing ReLU
nonlinearities into the product of an identity and a Heaviside step function,
(ii) introducing a separate path that decomposes the neural net expectation
from its variance. We demonstrate formally that introducing separate latent
binary variables to the activations allows representing the neural network
likelihood as a chain of linear operations. Performing variational inference on
this construction enables a sampling-free computation of the evidence lower
bound which is a more effective approximation than the widely applied Monte
Carlo sampling and CLT related techniques. We evaluate the model on a range of
regression and classification tasks against BNN inference alternatives, showing
competitive or improved performance over the current state-of-the-art.

15
The categories of this paper are: nucl-ex
29
The categories of this paper are: funct-an
27
The categories of this paper are: comp-gas
34
The categories of this paper are: chem-ph
13
The categories of this paper are: quant-ph
25
The categories of this paper are: patt-sol
35
The categories of this paper are: mtrl-th
6
The categories of this paper are: astro-ph
18
The categories of this paper are: econ
23
The categories of this paper are: solv-int
7
The categories of this paper are: hep-th
26
The categories of this paper are: bayes-an
12
The categories of this paper are: nucl-th
32
The categories of this paper are: atom-ph
22
The categories of this paper are: chao-dyn
9
The categories of this paper are: nlin