Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Robust Deep Learning from Crowds with Belief Propagation   Crowdsourcing systems enable us to collect large-scale dataset, but
inherently suffer from noisy labels of low-paid workers. We address the
inference and learning problems using such a crowdsourced dataset with noise.
Due to the nature of sparsity in crowdsourcing, it is critical to exploit both
probabilistic model to capture worker prior and neural network to extract task
feature despite risks from wrong prior and overfitted feature in practice. We
hence establish a neural-powered Bayesian framework, from which we devise
deepMF and deepBP with different choice of variational approximation methods,
mean field (MF) and belief propagation (BP), respectively. This provides a
unified view of existing methods, which are special cases of deepMF with
different priors. In addition, our empirical study suggests that deepBP is a
new approach, which is more robust against wrong prior, feature overfitting and
extreme workers thanks to the more sophisticated BP than MF.

7
The categories of this paper are: hep-th
19
The categories of this paper are: eess
0
The categories of this paper are: hep-ph
31
The categories of this paper are: ao-sci
2
The categories of this paper are: math
29
The categories of this paper are: funct-an
23
The categories of this paper are: solv-int
25
The categories of this paper are: patt-sol
12
The categories of this paper are: nucl-th
13
The categories of this paper are: quant-ph
8
The categories of this paper are: hep-ex
24
The categories of this paper are: dg-ga
28
The categories of this paper are: alg-geom
5
The categories of this paper are: gr-qc
10
The categories of this paper are: q-bio
14
The categories of this paper are: math-ph