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Found 7 papers, 2 papers with code
Data-Driven Modeling and Verification of Perception-Based Autonomous Systems
This paper addresses the problem of data-driven modeling and verification of perception-based autonomous systems.
Credal Bayesian Deep Learning
We show that CBDL is better at quantifying and disentangling different types of uncertainties than single BNNs, ensemble of BNNs, and Bayesian Model Averaging.
Risk Verification of Stochastic Systems with Neural Network Controllers
Motivated by the fragility of neural network (NN) controllers in safety-critical applications, we present a data-driven framework for verifying the risk of stochastic dynamical systems with NN controllers.
Confidence Composition for Monitors of Verification Assumptions
To predict safety violations in a verified system, we propose a three-step confidence composition (CoCo) framework for monitoring verification assumptions.
ModelGuard: Runtime Validation of Lipschitz-continuous Models
This paper presents ModelGuard, a sampling-based approach to runtime model validation for Lipschitz-continuous models.
Confidence Calibration with Bounded Error Using Transformations
As machine learning techniques become widely adopted in new domains, especially in safety-critical systems such as autonomous vehicles, it is crucial to provide accurate output uncertainty estimation.
Verisig: verifying safety properties of hybrid systems with neural network controllers
This paper presents Verisig, a hybrid system approach to verifying safety properties of closed-loop systems using neural networks as controllers.
Systems and Control
