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Found 11 papers, 5 papers with code
Scenic: A Language for Scenario Specification and Data Generation
We design a domain-specific language, Scenic, for describing scenarios that are distributions over scenes and the behaviors of their agents over time.
Counterexample-Guided Synthesis of Perception Models and Control
The framework uses a falsifier to find counterexamples, or traces of the systems that violate a safety property, to extract information that enables efficient modeling of the perception modules and errors in it.
A Formalization of Robustness for Deep Neural Networks
The process of generating the perturbations that expose the lack of robustness of neural networks is known as adversarial input generation.
VERIFAI: A Toolkit for the Design and Analysis of Artificial Intelligence-Based Systems
We present VERIFAI, a software toolkit for the formal design and analysis of systems that include artificial intelligence (AI) and machine learning (ML) components.
Scenic: A Language for Scenario Specification and Scene Generation
We propose a new probabilistic programming language for the design and analysis of perception systems, especially those based on machine learning.
SOTER: A Runtime Assurance Framework for Programming Safe Robotics Systems
SOTER provides language primitives to declaratively construct a RTA module consisting of an advanced, high-performance controller (uncertified), a safe, lower-performance controller (certified), and the desired safety specification.
Counterexample-Guided Data Augmentation
We present a novel framework for augmenting data sets for machine learning based on counterexamples.
Time Series Learning using Monotonic Logical Properties
Cyber-physical systems of today are generating large volumes of time-series data.
Verifying Controllers Against Adversarial Examples with Bayesian Optimization
We specify safety constraints using logic and exploit structure in the problem in order to test the system for adversarial counter examples that violate the safety specifications.
Context-Specific Validation of Data-Driven Models
We propose a context-specific validation framework to quantify the quality of a learned model based on a distance measure between the closed-loop actual system and the learned model.
Systematic Testing of Convolutional Neural Networks for Autonomous Driving
We present a framework to systematically analyze convolutional neural networks (CNNs) used in classification of cars in autonomous vehicles.
