Gosia Lazuka, Andreea Simona Anghel, et al.
SC 2024
Discovering interpretable patterns for classification of sequential data is of key im- portance for a variety of fields, ranging from genomics to fraud detection or more generally interpretable decision-making. In this paper, we propose a novel differ- entiable fully interpretable method to discover both local and global patterns (i.e. catching a relative or absolute temporal dependency) for rule-based binary classi- fication. It consists of a convolutional binary neural network with an interpretable neural filter and a training strategy based on dynamically-enforced sparsity. We demonstrate the validity and usefulness of the approach on synthetic datasets and on an open-source peptides dataset. Key to this end-to-end differentiable method is that the expressive patterns used in the rules are learned alongside the rules themselves.
Gosia Lazuka, Andreea Simona Anghel, et al.
SC 2024
Yidi Wu, Thomas Bohnstingl, et al.
ICML 2025
Ben Fei, Jinbai Liu
IEEE Transactions on Neural Networks
Robert Farrell, Rajarshi Das, et al.
AAAI-SS 2010