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cs.LG2026
Embedding interpretable -regression into neural networks for uncovering temporal structure in cell imaging
Fabian Kabus, Maren Hackenberg, Julia Hindel +6
While artificial neural networks excel in unsupervised learning of non-sparse structure, classical statistical regression techniques offer better interpretability, in particular wh…
cs.LG2024
Constrained Reinforcement Learning for Safe Heat Pump Control
Baohe Zhang, Lilli Frison, Thomas Brox +1
Constrained Reinforcement Learning (RL) has emerged as a significant research area within RL, where integrating constraints with rewards is crucial for enhancing safety and perform…