Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Teraflop-scale Incremental Machine Learning   We propose a long-term memory design for artificial general intelligence
based on Solomonoff's incremental machine learning methods. We use R5RS Scheme
and its standard library with a few omissions as the reference machine. We
introduce a Levin Search variant based on Stochastic Context Free Grammar
together with four synergistic update algorithms that use the same grammar as a
guiding probability distribution of programs. The update algorithms include
adjusting production probabilities, re-using previous solutions, learning
programming idioms and discovery of frequent subprograms. Experiments with two
training sequences demonstrate that our approach to incremental learning is
effective.

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The categories of this paper are: mtrl-th
24
The categories of this paper are: dg-ga
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The categories of this paper are: supr-con
23
The categories of this paper are: solv-int
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The categories of this paper are: chao-dyn
26
The categories of this paper are: bayes-an
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The categories of this paper are: comp-gas
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The categories of this paper are: plasm-ph
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The categories of this paper are: nlin
5
The categories of this paper are: gr-qc
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The categories of this paper are: hep-lat
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The categories of this paper are: q-fin
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The categories of this paper are: stat
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The categories of this paper are: hep-ex
2
The categories of this paper are: math
25
The categories of this paper are: patt-sol