Compositionality Unlocks Deep Interpretable Models

• URL: http://arxiv.org/abs/2504.02667v1
• Date: Thu, 03 Apr 2025 15:07:54 GMT
• Title: Compositionality Unlocks Deep Interpretable Models
• Abstract summary: $chi$-net is an intrinsically interpretable architecture combining the compositional multilinear structure of tensor networks with the expressivity and efficiency of deep neural networks.
• Score: 0.5437298646956507
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose $\chi$-net, an intrinsically interpretable architecture combining the compositional multilinear structure of tensor networks with the expressivity and efficiency of deep neural networks. $\chi$-nets retain equal accuracy compared to their baseline counterparts. Our novel, efficient diagonalisation algorithm, ODT, reveals linear low-rank structure in a multilayer SVHN model. We leverage this toward formal weight-based interpretability and model compression.

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