Searching for Minimal Neural Networks in Fourier Space
Year: 2010
Type: conference-paper
Abstract: The principle of minimum description length suggests looking for the simplest network that works well on the training examples, where simplicity is measured by network description size based on a reasonable programming language for encoding networks. Previous work used an assembler-like universal network encoding language (NEL) and Speed Priorbased search (related to Levin’s Universal Search) to quickly find low-complexity nets with excellent generalization performance. Here we define a more natural and often more practical NEL whose instructions are frequency domain coefficients. Frequency coefficients may get encoded by few bits, hence huge weight matrices may just be low-complexity superpositions of patterns computed by programs with few elementary instructions. On various benchmarks this weight matrix encoding greatly accelerates the search. The scheme was tested on pole-balancing, long-term dependency T-maze, and ball throwing. Some of the solutions turn out to be unexpectedly simple as they are computable by fairly short bit strings.
Source: Artificial General Intelligence
DOI: 10.2991/agi.2010.28
Institution: Università della Svizzera italiana
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W1968113145