publications

2024

1. CoLoRAI

   

   Compositionality Unlocks Deep Interpretable Models

   Thomas Dooms, Ward Gauderis, Geraint Wiggins, and Jose Antonio Oramas Mogrovejo

   In Connecting Low-Rank Representations in AI: At the 39th Annual AAAI Conference on Artificial Intelligence, Nov 2024

   Abs arXiv Bib HTML Video Poster Slides

   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 weightbased interpretability and model compression.

   @inproceedings{dooms_compositionality_2024,
     title = {Compositionality {Unlocks} {Deep} {Interpretable} {Models}},
     url = {https://openreview.net/forum?id=bXAt5iZ69l},
     urldate = {2025-02-17},
     booktitle = {Connecting {Low}-{Rank} {Representations} in {AI}: {At} the 39th {Annual} {AAAI} {Conference} on {Artificial} {Intelligence}},
     author = {Dooms, Thomas and Gauderis, Ward and Wiggins, Geraint and Mogrovejo, Jose Antonio Oramas},
     month = nov,
     year = {2024},
   }