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iFuzz-Meta: An Interpretable Fuzzy Learning Framework Bridging Top-Down and Bottom-Up Knowledge Integration

2025-08-12

Abstract excerpt

<p>Interpretable representation learning remains a key challenge in modern neural computation, particularly when models are expected not only to perform but also to explain their reasoning. This paper introduces iFuzz-Meta, an interpretable fuzzy rule-based learning framework that preserves human-understandable reasoning structures within modern neural architectures. Each fuzzy rule corresponds to a semantic and s...

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Literature Corpus work: 2c8f35bc-7b24-5c0a-9e85-508d530f2834
DOI: 10.31234/osf.io/pskfj_v1

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