Parselets: An Abstraction for Fast, General-Purpose Algorithmic Information Calculus
Fran\c{c}ois Cayre

TL;DR
This paper introduces parselets, a recursive data structure that enables fast, accurate, and general-purpose algorithmic information measures on finite string multisets through compression, modeling, and iterative refinement.
Contribution
The work presents a novel framework using parselets for explicit modeling and compression, embodying principles like Occam's Razor, to compute information measures efficiently.
Findings
Parselets enable fast computation of information measures.
The framework produces minimal sufficient models of data.
Comparison with standard compressors demonstrates effectiveness.
Abstract
This work describes the principled design of a theoretical framework leading to fast and accurate algorithmic information measures on finite multisets of finite strings by means of compression. One distinctive feature of our approach is to manipulate {\em reified}, explicit representations of the very entities and quantities of the theory itself: compressed strings, models, rate-distortion states, minimal sufficient models, joint and relative complexity. To do so, a programmable, recursive data structure called a {\em parselet} essentially provides modeling of a string as a concatenation of parameterized instantiations from sets of finite strings that encode the regular part of the data. This supports another distinctive feature of this work, which is the native embodiment of Epicurus' Principle on top of Occam's Razor, so as to produce both a most-significant and most-general explicit…
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Taxonomy
TopicsNeural Networks and Applications
