paper-with-me

Papers

Grounding Occam's Razor in a Formal Theory of Simplicity

2020-04-11 · Ben Goertzel

A formal theory of simplicity is introduced, in the context of a "combinational" computation model that views computation as comprising the iterated transformational and compositional activity of a population of agents upon each other. Conventional measures of simplicity in terms of algorithmic information etc. are shown to be special cases of a broader understanding of the core "symmetry" properties constituting what is defined here as a Compositional Simplicity Measure (CoSM). This theory of CoSMs is extended to a theory of CoSMOS (Combinational Simplicity Measure Operating Sets) which involve multiple simplicity measures utilized together. Given a vector of simplicity measures, an entity is associated not with an individual simplicity value but with a "simplicity bundles" of Pareto-optimal simplicity-value vectors. CoSMs and CoSMOS are then used as a foundation for a theory of pattern and multipattern, and a theory of hierarchy and heterarchy in systems of patterns. A formalization of the cognitive-systems notion of a "coherent dual network" interweaving hierarchy and heterarchy in a consistent way is presented. The high level end result of this investigation is to re-envision Occam's Razor as something like: When in doubt, prefer hypotheses whose simplicity bundles are Pareto optimal, partly because doing so both permits and benefits from the construction of coherent dual networks comprising coordinated and consistent multipattern hierarchies and heterarchies.

📄 PDF Abstract BibTeX arXiv:2004.05269

Code (0)

등록된 구현이 없습니다.

Similar Papers 제목 키워드 기반

Statistical learning theory and Occam's razor: The core argument

2023-12-21 · Tom F. Sterkenburg

Statistical learning theory is often associated with the principle of Occam's razor, which recommends a simplicity preference in inductive inference. This paper distills the core argument for simplicity obtainable from s…

Learning Theory

Benign interpolation and Occam's razor

2026-08-04 · Tom F. Sterkenburg, Daniel A. Herrmann, Jan-Willem Romeijn arxiv

Contemporary deep learning methods generalize well even when they fit their training data perfectly, a phenomenon known as benign interpolation. This phenomenon cannot be accounted for by classical statistical learning t…

In-context learning and Occam's razor

2024-10-17 · Eric Elmoznino, Tom Marty, Tejas Kasetty, Leo Gagnon 외

A central goal of machine learning is generalization. While the No Free Lunch Theorem states that we cannot obtain theoretical guarantees for generalization without further assumptions, in practice we observe that simple…

Data CompressionIn-Context Learning

Occam's razor is insufficient to infer the preferences of irrational agents

2017-12-15 · NeurIPS 2018 12 · Stuart Armstrong, Sören Mindermann

Inverse reinforcement learning (IRL) attempts to infer human rewards or preferences from observed behavior. Since human planning systematically deviates from rationality, several approaches have been tried to account for…

Reinforcement LearningReinforcement Learning (RL)

The Geometric Occam's Razor Implicit in Deep Learning

2021-11-30 · Benoit Dherin, Michael Munn, David G. T. Barrett

In over-parameterized deep neural networks there can be many possible parameter configurations that fit the training data exactly. However, the properties of these interpolating solutions are poorly understood. We argue …

ARCDeep Learning