Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Inference in topic models: sparsity and trade-off   Topic models are popular for modeling discrete data (e.g., texts, images,
videos, links), and provide an efficient way to discover hidden
structures/semantics in massive data. One of the core problems in this field is
the posterior inference for individual data instances. This problem is
particularly important in streaming environments, but is often intractable. In
this paper, we investigate the use of the Frank-Wolfe algorithm (FW) for
recovering sparse solutions to posterior inference. From detailed elucidation
of both theoretical and practical aspects, FW exhibits many interesting
properties which are beneficial to topic modeling. We then employ FW to design
fast methods, including ML-FW, for learning latent Dirichlet allocation (LDA)
at large scales. Extensive experiments show that to reach the same
predictiveness level, ML-FW can perform tens to thousand times faster than
existing state-of-the-art methods for learning LDA from massive/streaming data.

9
The categories of this paper are: nlin
35
The categories of this paper are: mtrl-th
0
The categories of this paper are: hep-ph
34
The categories of this paper are: chem-ph
12
The categories of this paper are: nucl-th
2
The categories of this paper are: math
23
The categories of this paper are: solv-int
15
The categories of this paper are: nucl-ex
33
The categories of this paper are: plasm-ph
10
The categories of this paper are: q-bio
27
The categories of this paper are: comp-gas
37
The categories of this paper are: supr-con
8
The categories of this paper are: hep-ex
26
The categories of this paper are: bayes-an
30
The categories of this paper are: q-alg
29
The categories of this paper are: funct-an