Similarity-based analogical proportions
- URL: http://arxiv.org/abs/2402.18360v1
- Date: Wed, 28 Feb 2024 14:31:34 GMT
- Title: Similarity-based analogical proportions
- Authors: Christian Anti\'c
- Abstract summary: The purpose of this paper is to build a bridge from similarity to analogical proportions by formulating the latter in terms of the former.
The benefit of this similarity-based approach is that the connection between proportions and similarity is built into the framework.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The author has recently introduced abstract algebraic frameworks of
analogical proportions and similarity within the general setting of universal
algebra. The purpose of this paper is to build a bridge from similarity to
analogical proportions by formulating the latter in terms of the former. The
benefit of this similarity-based approach is that the connection between
proportions and similarity is built into the framework and therefore evident
which is appealing since proportions and similarity are both at the center of
analogy; moreover, future results on similarity can directly be applied to
analogical proportions.
Related papers
- Cluster-Aware Similarity Diffusion for Instance Retrieval [64.40171728912702]
Diffusion-based re-ranking is a common method used for retrieving instances by performing similarity propagation in a nearest neighbor graph.
We propose a novel Cluster-Aware Similarity (CAS) diffusion for instance retrieval.
arXiv Detail & Related papers (2024-06-04T14:19:50Z) - Interpretable Measures of Conceptual Similarity by
Complexity-Constrained Descriptive Auto-Encoding [112.0878081944858]
Quantifying the degree of similarity between images is a key copyright issue for image-based machine learning.
We seek to define and compute a notion of "conceptual similarity" among images that captures high-level relations.
Two highly dissimilar images can be discriminated early in their description, whereas conceptually dissimilar ones will need more detail to be distinguished.
arXiv Detail & Related papers (2024-02-14T03:31:17Z) - Generalization-baed similarity [0.0]
We develop an abstract notion of similarity based on the observation that sets of generalizations encode important properties of elements.
We show that similarity defined in this way has appealing mathematical properties.
arXiv Detail & Related papers (2023-02-13T14:48:59Z) - Some recent advances in reasoning based on analogical proportions [9.861775841965386]
Analogical proportions play a key role in the formalization of analogical inference.
The paper first discusses how to improve analogical inference in terms of accuracy and in terms of computational cost.
arXiv Detail & Related papers (2022-12-22T14:10:14Z) - Counting Like Human: Anthropoid Crowd Counting on Modeling the
Similarity of Objects [92.80955339180119]
mainstream crowd counting methods regress density map and integrate it to obtain counting results.
Inspired by this, we propose a rational and anthropoid crowd counting framework.
arXiv Detail & Related papers (2022-12-02T07:00:53Z) - Evaluation of taxonomic and neural embedding methods for calculating
semantic similarity [0.0]
We study the mechanisms between taxonomic and distributional similarity measures.
We find that taxonomic similarity measures can depend on the shortest path length as a prime factor to predict semantic similarity.
The synergy of retrofitting neural embeddings with concept relations in similarity prediction may indicate a new trend to leverage knowledge bases on transfer learning.
arXiv Detail & Related papers (2022-09-30T02:54:21Z) - Attributable Visual Similarity Learning [90.69718495533144]
This paper proposes an attributable visual similarity learning (AVSL) framework for a more accurate and explainable similarity measure between images.
Motivated by the human semantic similarity cognition, we propose a generalized similarity learning paradigm to represent the similarity between two images with a graph.
Experiments on the CUB-200-2011, Cars196, and Stanford Online Products datasets demonstrate significant improvements over existing deep similarity learning methods.
arXiv Detail & Related papers (2022-03-28T17:35:31Z) - MNet-Sim: A Multi-layered Semantic Similarity Network to Evaluate
Sentence Similarity [0.0]
Similarity is a comparative-subjective measure that varies with the domain within which it is considered.
This paper presents a multi-layered semantic similarity network model built upon multiple similarity measures.
It is shown to have demonstrated better performance scores in assessing sentence similarity.
arXiv Detail & Related papers (2021-11-09T20:43:18Z) - Few-shot Visual Reasoning with Meta-analogical Contrastive Learning [141.2562447971]
We propose to solve a few-shot (or low-shot) visual reasoning problem, by resorting to analogical reasoning.
We extract structural relationships between elements in both domains, and enforce them to be as similar as possible with analogical learning.
We validate our method on RAVEN dataset, on which it outperforms state-of-the-art method, with larger gains when the training data is scarce.
arXiv Detail & Related papers (2020-07-23T14:00:34Z) - Pairwise Supervision Can Provably Elicit a Decision Boundary [84.58020117487898]
Similarity learning is a problem to elicit useful representations by predicting the relationship between a pair of patterns.
We show that similarity learning is capable of solving binary classification by directly eliciting a decision boundary.
arXiv Detail & Related papers (2020-06-11T05:35:16Z) - Analogy as Nonparametric Bayesian Inference over Relational Systems [10.736626320566705]
We propose a Bayesian model that generalizes relational knowledge to novel environments by analogically weighting predictions from previously encountered relational structures.
We show that this learner outperforms a naive, theory-based learner on relational data derived from random- and Wikipedia-based systems when experience with the environment is small.
arXiv Detail & Related papers (2020-06-07T14:07:46Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.