STL robustness is used as a differentiable loss to train biomolecular neural networks for regression and closed-loop control in silico.
Predicting experimental sepsis survival with a mathematical model of acute inflammation,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
STL-based Optimization of Biomolecular Neural Networks for Regression and Control
STL robustness is used as a differentiable loss to train biomolecular neural networks for regression and closed-loop control in silico.