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Found 6 papers, 1 papers with code
Formal Verification of Long Short-Term Memory based Audio Classifiers: A Star based Approach
Formally verifying audio classification systems is essential to ensure accurate signal classification across real-world applications like surveillance, automotive voice commands, and multimedia content management, preventing potential errors with serious consequences.
Robustness Verification of Deep Neural Networks using Star-Based Reachability Analysis with Variable-Length Time Series Input
This paper presents a case study of the robustness verification approach for time series regression NNs (TSRegNN) using set-based formal methods.
Work In Progress: Safety and Robustness Verification of Autoencoder-Based Regression Models using the NNV Tool
This work in progress paper introduces robustness verification for autoencoder-based regression neural network (NN) models, following state-of-the-art approaches for robustness verification of image classification NNs.
Ablation Study of How Run Time Assurance Impacts the Training and Performance of Reinforcement Learning Agents
Reinforcement Learning (RL) has become an increasingly important research area as the success of machine learning algorithms and methods grows.
Neural Network Repair with Reachability Analysis
Formally verifying the safety and robustness of well-trained DNNs and learning-enabled systems under attacks, model uncertainties, and sensing errors is essential for safe autonomy.
Reachability Analysis of Convolutional Neural Networks
Besides the computation of reachable sets, our approach is also capable of backtracking to the input domain given an output reachable set.
