Interval LSTM and NODE models trained with cascade or joint strategies deliver uncertainty-aware predictions for system identification via interval arithmetic.
Understanding certified training with interval bound propagation,
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 2verdicts
UNVERDICTED 2roles
background 1polarities
support 1representative citing papers
Tutorial introducing applications of the existing α,β-CROWN verifier to scalable formal verification of neural network controllers via bound computation and domain partitioning.
citing papers explorer
-
Beyond Prediction: Interval Neural Networks for Uncertainty-Aware System Identification
Interval LSTM and NODE models trained with cascade or joint strategies deliver uncertainty-aware predictions for system identification via interval arithmetic.
-
Bridging Control with Neural Network Verifier alpha-beta-CROWN: A Tutorial
Tutorial introducing applications of the existing α,β-CROWN verifier to scalable formal verification of neural network controllers via bound computation and domain partitioning.