Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Nonparametric Bayesian Deep Networks with Local Competition   The aim of this work is to enable inference of deep networks that retain high
accuracy for the least possible model complexity, with the latter deduced from
the data during inference. To this end, we revisit deep networks that comprise
competing linear units, as opposed to nonlinear units that do not entail any
form of (local) competition. In this context, our main technical innovation
consists in an inferential setup that leverages solid arguments from Bayesian
nonparametrics. We infer both the needed set of connections or locally
competing sets of units, as well as the required floating-point precision for
storing the network parameters. Specifically, we introduce auxiliary discrete
latent variables representing which initial network components are actually
needed for modeling the data at hand, and perform Bayesian inference over them
by imposing appropriate stick-breaking priors. As we experimentally show using
benchmark datasets, our approach yields networks with less computational
footprint than the state-of-the-art, and with no compromises in predictive
accuracy.

4
The categories of this paper are: cond-mat
11
The categories of this paper are: hep-lat
14
The categories of this paper are: math-ph
10
The categories of this paper are: q-bio
9
The categories of this paper are: nlin
30
The categories of this paper are: q-alg
2
The categories of this paper are: math
18
The categories of this paper are: econ
13
The categories of this paper are: quant-ph
25
The categories of this paper are: patt-sol
26
The categories of this paper are: bayes-an
8
The categories of this paper are: hep-ex
23
The categories of this paper are: solv-int
37
The categories of this paper are: supr-con
27
The categories of this paper are: comp-gas
6
The categories of this paper are: astro-ph