Symmetric Divergence and Normalized Similarity: A Unified Topological Framework for Representation Analysis

SUMMARY

New topological framework addresses key limitations in neural representation comparison by introducing symmetric divergences and normalized similarity measures that remain bounded regardless of sample size, enabling reliable cross-scenario benchmarking #ml #ai

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