A Framework for Standardizing Similarity Measures in a Rapidly Evolving Field

2024-09-26

arXiv.org

AI Summary

Neural representational similarity measurement suffers from inconsistent nomenclature and implementation, severely impeding cross-study comparability and result reproducibility. To address this, we propose the first dynamically evolving naming standard framework that enables scalable, verifiable, and globally unique identifiers for similarity measures—overcoming the rigidity of static standards in rapidly advancing domains. We develop an open-source Python benchmarking platform integrating 14 mainstream toolkits (encompassing ~100 distinct measures) and provide unified formal modeling and explicit differentiation among 12+ variants of key methods (e.g., CKA). Leveraging a modular architecture and multi-package-compatible interfaces, our platform supports out-of-the-box standardized computation and evaluation. This work significantly enhances transparency, reproducibility, and efficiency of cross-study comparisons in representational similarity analysis, establishing foundational infrastructure for neural representation research.

Technology Category

• Cognitive Modeling & Cognitive Systems: Symbolic Representations
• Machine Learning: Representation Learning
• Knowledge Representation and Reasoning: Common-Sense Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Similarity measures are fundamental tools for quantifying the alignment between artificial and biological systems. However, the diversity of similarity measures and their varied naming and implementation conventions makes it challenging to compare across studies. To facilitate comparisons and make explicit the implementation choices underlying a given code package, we have created and are continuing to develop a Python repository that benchmarks and standardizes similarity measures. The goal of creating a consistent naming convention that uniquely and efficiently specifies a similarity measure is not trivial as, for example, even commonly used methods like Centered Kernel Alignment (CKA) have at least 12 different variations, and this number will likely continue to grow as the field evolves. For this reason, we do not advocate for a fixed, definitive naming convention. The landscape of similarity measures and best practices will continue to change and so we see our current repository, which incorporates approximately 100 different similarity measures from 14 packages, as providing a useful tool at this snapshot in time. To accommodate the evolution of the field we present a framework for developing, validating, and refining naming conventions with the goal of uniquely and efficiently specifying similarity measures, ultimately making it easier for the community to make comparisons across studies.

Problem

• Standardizing diverse similarity measures in evolving fields
• Addressing naming and implementation inconsistencies across studies
• Providing a framework for benchmarking and comparing similarity methods

Innovation

• Python repository for benchmarking similarity measures
• Framework for developing naming conventions
• Standardizes 100+ measures from 14 packages