Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Growing Long-term Episodic & Semantic Memory   The long-term memory of most connectionist systems lies entirely in the
weights of the system. Since the number of weights is typically fixed, this
bounds the total amount of knowledge that can be learned and stored. Though
this is not normally a problem for a neural network designed for a specific
task, such a bound is undesirable for a system that continually learns over an
open range of domains. To address this, we describe a lifelong learning system
that leverages a fast, though non-differentiable, content-addressable memory
which can be exploited to encode both a long history of sequential episodic
knowledge and semantic knowledge over many episodes for an unbounded number of
domains. This opens the door for investigation into transfer learning, and
leveraging prior knowledge that has been learned over a lifetime of experiences
to new domains.

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The categories of this paper are: hep-th
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The categories of this paper are: patt-sol
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The categories of this paper are: math-ph
17
The categories of this paper are: q-fin
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The categories of this paper are: solv-int
5
The categories of this paper are: gr-qc
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The categories of this paper are: cmp-lg
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The categories of this paper are: eess
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The categories of this paper are: atom-ph
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The categories of this paper are: econ
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The categories of this paper are: chao-dyn
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The categories of this paper are: supr-con
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The categories of this paper are: chem-ph
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The categories of this paper are: q-bio
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The categories of this paper are: alg-geom
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The categories of this paper are: astro-ph