arXiv:2608. 14646v1 Announce Type: cross Abstract: Interpretable representation learning remains a key challenge in modern neural computation, particularly when models are expected not only to perform but also to explain their reasoning.
By Xiaowei Jiang, Daniel Leong, Beining Cao, Nan Zhou, Yingtao Ren, Yu-Cheng Chang, Thomas Do, Chin-Teng Lin
arXiv:2609. 20194v1 Announce Type: cross Abstract: Triangular membership functions (MFs) are widely used in fuzzy systems because of their interpretability, low parameterization complexity, and strong locality properties.
By Babak Sarani, Rahman Ardakanian, Ali Mousavi
arXiv:2605. 04193v2 Announce Type: replace Abstract: Inductive Logic Programming (ILP) aims to learn interpretable first-order rules from data, but existing symbolic and neuro-symbolic approaches struggle to scale to noisy and probabilistic settings.
By Iman Sharifi, Peng Wei, Saber Fallah
arXiv:2607. 20277v1 Announce Type: new Abstract: Machine learning models achieve high predictive accuracy in regression tasks, but their deployment in safety-critical and regulated domains requires interpretability.
By Cayan Deniz Kucuktopana, Javier Fumanal-Idocin, Richard Pitts, Javier Andreu-Perez
NeuroRule is a knowledge distillation framework that transforms high‑capacity neural networks into explainable rule‑sets. It adapts the EVOTER rule‑set evolution infrastructure to evolve propositional logic expressions that capture the neural network’s performance. The approach includes a conciseness objective to enhance explainability and demonstrates viability even without access to the original training data.
By Tapaswini Kodavanti, Hormoz Shahrzad, Risto Miikkulainen
arXiv:2609.24358v1 Announce Type: new
Abstract: Predicting when maritime systems require maintenance can be critical, avoiding hazards and costly consequences. To address this problem, this paper pro...
By Dionisis Kalogeropoulos, Georgia Sovatzidi, Dimitris K. Iakovidis