June 11, 2024, 4:47 a.m. | Susu Sun, Stefano Woerner, Andreas Maier, Lisa M. Koch, Christian F. Baumgartner

cs.LG updates on arXiv.org arxiv.org

arXiv:2406.05477v1 Announce Type: cross
Abstract: Interpretability is crucial for machine learning algorithms in high-stakes medical applications. However, high-performing neural networks typically cannot explain their predictions. Post-hoc explanation methods provide a way to understand neural networks but have been shown to suffer from conceptual problems. Moreover, current research largely focuses on providing local explanations for individual samples rather than global explanations for the model itself. In this paper, we propose Attri-Net, an inherently interpretable model for multi-label classification that provides local …

abstract algorithms applications arxiv class classification cs.cv cs.lg current however interpretability machine machine learning machine learning algorithms medical networks neural networks predictions type

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