arXiv:2205.07635
Relating Information and Proof
원문 초록
In mathematics information is a number that measures uncertainty (entropy) based on a probabilistic distribution, often of an obscure origin. In real life language information is a datum, a statement, more precisely, a formula. But such a formula should be justified by a proof. I try to formalize this perception of information. The measure of informativeness of a proof is based on the set of proofs related to the formulas under consideration. This set of possible proofs (`a knowledge base') defines a probabilistic measure, and entropic weight is defined using this measure. The paper is mainly conceptual, it is not clear where and how this approach can be applied.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Multi-Preference Lambda-weighted Listwise DPO for Dynamic Preference Alignment
CuRe: Cultural Gaps in the Long Tail of Text-to-Image Systems
ERU-KG: Efficient Reference-aligned Unsupervised Keyphrase Generation
Power-Law Decay Loss for Large Language Model Finetuning: Focusing on Information Sparsity to Enhance Generation Quality
ICYM2I: The illusion of multimodal informativeness under missingness
Laplace Sample Information: Data Informativeness Through a Bayesian Lens
토론
이 논문과 연결된 포럼 글이 아직 없습니다.