no code implementations • 16 Nov 2023 • Neelanjana Pal, Taylor T Johnson
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.
1 code implementation • 26 Jul 2023 • Neelanjana Pal, Diego Manzanas Lopez, Taylor T Johnson
This paper presents a case study of the robustness verification approach for time series regression NNs (TSRegNN) using set-based formal methods.
no code implementations • 14 Jul 2022 • Neelanjana Pal, Taylor T Johnson
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.
no code implementations • 8 Jul 2022 • Nathaniel Hamilton, Kyle Dunlap, Taylor T Johnson, Kerianne L Hobbs
Reinforcement Learning (RL) has become an increasingly important research area as the success of machine learning algorithms and methods grows.
no code implementations • 9 Aug 2021 • Xiaodong Yang, Tom Yamaguchi, Hoang-Dung Tran, Bardh Hoxha, Taylor T Johnson, Danil Prokhorov
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.
no code implementations • 22 Jun 2021 • Xiaodong Yang, Tomoya Yamaguchi, Hoang-Dung Tran, Bardh Hoxha, Taylor T Johnson, Danil Prokhorov
Besides the computation of reachable sets, our approach is also capable of backtracking to the input domain given an output reachable set.