Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Text-mining the NeuroSynth corpus using Deep Boltzmann Machines   Large-scale automated meta-analysis of neuroimaging data has recently
established itself as an important tool in advancing our understanding of human
brain function. This research has been pioneered by NeuroSynth, a database
collecting both brain activation coordinates and associated text across a large
cohort of neuroimaging research papers. One of the fundamental aspects of such
meta-analysis is text-mining. To date, word counts and more sophisticated
methods such as Latent Dirichlet Allocation have been proposed. In this work we
present an unsupervised study of the NeuroSynth text corpus using Deep
Boltzmann Machines (DBMs). The use of DBMs yields several advantages over the
aforementioned methods, principal among which is the fact that it yields both
word and document embeddings in a high-dimensional vector space. Such
embeddings serve to facilitate the use of traditional machine learning
techniques on the text corpus. The proposed DBM model is shown to learn
embeddings with a clear semantic structure.

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The categories of this paper are: hep-th
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The categories of this paper are: ao-sci
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The categories of this paper are: physics
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The categories of this paper are: nucl-th
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The categories of this paper are: eess
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The categories of this paper are: plasm-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: q-fin
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The categories of this paper are: solv-int
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The categories of this paper are: math-ph
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The categories of this paper are: nlin
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The categories of this paper are: gr-qc
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The categories of this paper are: chem-ph
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The categories of this paper are: quant-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: astro-ph