paper-with-me

Papers

Random Walk in Random Permutation Set Theory

2024-04-05 · Jiefeng Zhou, Zhen Li, Yong Deng

Random walk is an explainable approach for modeling natural processes at the molecular level. The Random Permutation Set Theory (RPST) serves as a framework for uncertainty reasoning, extending the applicability of Dempster-Shafer Theory. Recent explorations indicate a promising link between RPST and random walk. In this study, we conduct an analysis and construct a random walk model based on the properties of RPST, with Monte Carlo simulations of such random walk. Our findings reveal that the random walk generated through RPST exhibits characteristics similar to those of a Gaussian random walk and can be transformed into a Wiener process through a specific limiting scaling procedure. This investigation establishes a novel connection between RPST and random walk theory, thereby not only expanding the applicability of RPST, but also demonstrating the potential for combining the strengths of both approaches to improve problem-solving abilities.

📄 PDF Abstract BibTeX arXiv:2404.03978

Code (0)

등록된 구현이 없습니다.

Methods 이 논문이 사용한 방법론

SET Dynamic Sparse Training method where weight mask is updated randomly periodically

Similar Papers 제목 키워드 기반

Facilitating Graph Neural Networks with Random Walk on Simplicial Complexes

2023-10-30 · NeurIPS 2023 11 · Cai Zhou, Xiyuan Wang, Muhan Zhang

Node-level random walk has been widely used to improve Graph Neural Networks. However, there is limited attention to random walk on edge and, more generally, on $k$-simplices. This paper systematically analyzes how rando…

Human Memory Search as Initial-Visit Emitting Random Walk

2015-12-01 · NeurIPS 2015 12 · Kwang-Sung Jun, Jerry Zhu, Timothy T. Rogers, Zhuoran Yang 외

Imagine a random walk that outputs a state only when visiting it for the first time. The observed output is therefore a repeat-censored version of the underlying walk, and consists of a permutation of the states or a pre…

parameter estimation

Random Walks on Hypergraphs with Edge-Dependent Vertex Weights

2019-05-20 · Uthsav Chitra, Benjamin J. Raphael

Hypergraphs are used in machine learning to model higher-order relationships in data. While spectral methods for graphs are well-established, spectral theory for hypergraphs remains an active area of research. In this pa…

BIG-bench Machine Learning

The negation of permutation mass function

2024-03-11 · Yongchuan Tang, Rongfei Li

Negation is an important perspective of knowledge representation. Existing negation methods are mainly applied in probability theory, evidence theory and complex evidence theory. As a generalization of evidence theory, r…

Negation

Repeatable Random Permutation Set

2022-11-03 · Wenran Yang, Yong Deng

Random permutation set (RPS), as a recently proposed theory, enables powerful information representation by traversing all possible permutations. However, the repetition of items is not allowed in RPS while it is quite c…