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cs.LG2026
Evaluating Explanation Methods by the Predictors They Induce
Jacob Selbæk, Hugo L. Hammer
Explanations of machine learning models are usually judged by criteria that are hard to compare. We propose a simpler test: if an explanation really describes how a model uses its…
cs.LG2026
Improved Predictive Performance and Interpretability for Mesomorphic Neural Networks Using Local Fidelity Regularization
Hugo L. Hammer, Vajira Thambawita, Kristoffer Herland Hellton +1
Interpretable Mesomorphic Neural Networks (IMNs) offer a promising framework that combines the predictive power of deep neural networks with the interpretability of linear models.…
cs.LG2026
Knowledge-Guided Retrieval-Augmented Generation for Zero-Shot Psychiatric Data: Privacy Preserving Synthetic Data Generation
Adam Jakobsen, Sushant Gautam, Hugo Lewi Hammer +4
AI systems in healthcare research have shown potential to increase patient throughput and assist clinicians, yet progress is constrained by limited access to real patient data. To…