CoFrNets: Interpretable Neural Architecture Inspired by Continued Fractions
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912417179500544 |
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| author | Puri, Isha Dhurandhar, Amit Pedapati, Tejaswini Shanmugam, Kartikeyan Wei, Dennis Varshney, Kush R. |
| author_facet | Puri, Isha Dhurandhar, Amit Pedapati, Tejaswini Shanmugam, Kartikeyan Wei, Dennis Varshney, Kush R. |
| contents | In recent years there has been a considerable amount of research on local post hoc explanations for neural networks. However, work on building interpretable neural architectures has been relatively sparse. In this paper, we present a novel neural architecture, CoFrNet, inspired by the form of continued fractions which are known to have many attractive properties in number theory, such as fast convergence of approximations to real numbers. We show that CoFrNets can be efficiently trained as well as interpreted leveraging their particular functional form. Moreover, we prove that such architectures are universal approximators based on a proof strategy that is different than the typical strategy used to prove universal approximation results for neural networks based on infinite width (or depth), which is likely to be of independent interest. We experiment on nonlinear synthetic functions and are able to accurately model as well as estimate feature attributions and even higher order terms in some cases, which is a testament to the representational power as well as interpretability of such architectures. To further showcase the power of CoFrNets, we experiment on seven real datasets spanning tabular, text and image modalities, and show that they are either comparable or significantly better than other interpretable models and multilayer perceptrons, sometimes approaching the accuracies of state-of-the-art models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_05586 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CoFrNets: Interpretable Neural Architecture Inspired by Continued Fractions Puri, Isha Dhurandhar, Amit Pedapati, Tejaswini Shanmugam, Kartikeyan Wei, Dennis Varshney, Kush R. Machine Learning Artificial Intelligence In recent years there has been a considerable amount of research on local post hoc explanations for neural networks. However, work on building interpretable neural architectures has been relatively sparse. In this paper, we present a novel neural architecture, CoFrNet, inspired by the form of continued fractions which are known to have many attractive properties in number theory, such as fast convergence of approximations to real numbers. We show that CoFrNets can be efficiently trained as well as interpreted leveraging their particular functional form. Moreover, we prove that such architectures are universal approximators based on a proof strategy that is different than the typical strategy used to prove universal approximation results for neural networks based on infinite width (or depth), which is likely to be of independent interest. We experiment on nonlinear synthetic functions and are able to accurately model as well as estimate feature attributions and even higher order terms in some cases, which is a testament to the representational power as well as interpretability of such architectures. To further showcase the power of CoFrNets, we experiment on seven real datasets spanning tabular, text and image modalities, and show that they are either comparable or significantly better than other interpretable models and multilayer perceptrons, sometimes approaching the accuracies of state-of-the-art models. |
| title | CoFrNets: Interpretable Neural Architecture Inspired by Continued Fractions |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2506.05586 |