Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Computational Theory for Life-Long Learning of Semantics   Semantic vectors are learned from data to express semantic relationships
between elements of information, for the purpose of solving and informing
downstream tasks. Other models exist that learn to map and classify supervised
data. However, the two worlds of learning rarely interact to inform one another
dynamically, whether across types of data or levels of semantics, in order to
form a unified model. We explore the research problem of learning these vectors
and propose a framework for learning the semantics of knowledge incrementally
and online, across multiple mediums of data, via binary vectors. We discuss the
aspects of this framework to spur future research on this approach and problem.

11
The categories of this paper are: hep-lat
25
The categories of this paper are: patt-sol
8
The categories of this paper are: hep-ex
33
The categories of this paper are: plasm-ph
30
The categories of this paper are: q-alg
32
The categories of this paper are: atom-ph
37
The categories of this paper are: supr-con
22
The categories of this paper are: chao-dyn
34
The categories of this paper are: chem-ph
9
The categories of this paper are: nlin
21
The categories of this paper are: adap-org
15
The categories of this paper are: nucl-ex
29
The categories of this paper are: funct-an
14
The categories of this paper are: math-ph
18
The categories of this paper are: econ
6
The categories of this paper are: astro-ph