Neural Interpretable Reasoning

• URL: http://arxiv.org/abs/2502.11639v1
• Date: Mon, 17 Feb 2025 10:33:24 GMT
• Title: Neural Interpretable Reasoning
• Authors: Pietro Barbiero, Giuseppe Marra, Gabriele Ciravegna, David Debot, Francesco De Santis, Michelangelo Diligenti, Mateo Espinosa Zarlenga, Francesco Giannini,
• Abstract summary: We formalize a novel modeling framework for achieving interpretability in deep learning. We show that this complexity can be mitigated by treating interpretability as a Markovian property. We propose a new modeling paradigm -- neural generation and interpretable execution.
• Score: 12.106771300842945
• License:
• Abstract: We formalize a novel modeling framework for achieving interpretability in deep learning, anchored in the principle of inference equivariance. While the direct verification of interpretability scales exponentially with the number of variables of the system, we show that this complexity can be mitigated by treating interpretability as a Markovian property and employing neural re-parametrization techniques. Building on these insights, we propose a new modeling paradigm -- neural generation and interpretable execution -- that enables scalable verification of equivariance. This paradigm provides a general approach for designing Neural Interpretable Reasoners that are not only expressive but also transparent.

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