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Found 13 papers, 5 papers with code
Closed-loop Analysis of Vision-based Autonomous Systems: A Case Study
We address the above challenges by replacing the camera and the network with a compact probabilistic abstraction built from the confusion matrices computed for the DNN on a representative image data set.
An Overview of Structural Coverage Metrics for Testing Neural Networks
Deep neural network (DNN) models, including those used in safety-critical domains, need to be thoroughly tested to ensure that they can reliably perform well in different scenarios.
VPN: Verification of Poisoning in Neural Networks
Neural networks are successfully used in a variety of applications, many of them having safety and security concerns.
AntidoteRT: Run-time Detection and Correction of Poison Attacks on Neural Networks
For correction, we propose an input correction technique that uses a differential analysis to identify the trigger in the detected poisoned images, which is then reset to a neutral color.
QuantifyML: How Good is my Machine Learning Model?
The efficacy of machine learning models is typically determined by computing their accuracy on test data sets.
NNrepair: Constraint-based Repair of Neural Network Classifiers
We present novel strategies to enable precise yet efficient repair such as inferring correctness specifications to act as oracles for intermediate layer repair, and generation of experts for each class.
NEUROSPF: A tool for the Symbolic Analysis of Neural Networks
This paper presents NEUROSPF, a tool for the symbolic analysis of neural networks.
Fast, Structured Clinical Documentation via Contextual Autocomplete
We present a system that uses a learned autocompletion mechanism to facilitate rapid creation of semi-structured clinical documentation.
Parallelization Techniques for Verifying Neural Networks
Inspired by recent successes with parallel optimization techniques for solving Boolean satisfiability, we investigate a set of strategies and heuristics that aim to leverage parallel computing to improve the scalability of neural network verification.
A Programmatic and Semantic Approach to Explaining and DebuggingNeural Network Based Object Detectors
It is programmatic in that scenario representation is a program in a domain-specific probabilistic programming language which can be used to generate synthetic data to test a given perception module.
Property Inference for Deep Neural Networks
We present techniques for automatically inferring formal properties of feed-forward neural networks.
Compositional Verification for Autonomous Systems with Deep Learning Components
As autonomy becomes prevalent in many applications, ranging from recommendation systems to fully autonomous vehicles, there is an increased need to provide safety guarantees for such systems.
DeepSafe: A Data-driven Approach for Checking Adversarial Robustness in Neural Networks
We propose a novel approach for automatically identifying safe regions of the input space, within which the network is robust against adversarial perturbations.
