Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.02667 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909563467333632 |
|---|---|
| author | Dooms, Thomas Gauderis, Ward Wiggins, Geraint A. Oramas, Jose |
| author_facet | Dooms, Thomas Gauderis, Ward Wiggins, Geraint A. Oramas, Jose |
| contents | We propose $χ$-net, an intrinsically interpretable architecture combining the compositional multilinear structure of tensor networks with the expressivity and efficiency of deep neural networks. $χ$-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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_02667 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Compositionality Unlocks Deep Interpretable Models Dooms, Thomas Gauderis, Ward Wiggins, Geraint A. Oramas, Jose Machine Learning We propose $χ$-net, an intrinsically interpretable architecture combining the compositional multilinear structure of tensor networks with the expressivity and efficiency of deep neural networks. $χ$-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. |
| title | Compositionality Unlocks Deep Interpretable Models |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2504.02667 |