3 citations · 9 across the 6 of their papers we have counts for
7 papers · 1 filter
Safety Margins for Reinforcement Learning
Alexander Grushin, Walt Woods, Alvaro Velasquez +1
Any autonomous controller will be unsafe in some situations. The ability to quantitatively identify when these unsafe situations are about to occur is crucial for drawing timely hu…
Model-Free Robust Average-Reward Reinforcement Learning
Yue Wang, Alvaro Velasquez, George Atia +2
Robust Markov decision processes (MDPs) address the challenge of model uncertainty by optimizing the worst-case performance over an uncertainty set of MDPs. In this paper, we focus…
NoiseCAM: Explainable AI for the Boundary Between Noise and Adversarial Attacks
Wenkai Tan, Justus Renkhoff, Alvaro Velasquez +7
Deep Learning (DL) and Deep Neural Networks (DNNs) are widely used in various domains. However, adversarial attacks can easily mislead a neural network and lead to wrong decisions.…
Exploring Adversarial Attacks on Neural Networks: An Explainable Approach
Justus Renkhoff, Wenkai Tan, Alvaro Velasquez +7
Deep Learning (DL) is being applied in various domains, especially in safety-critical applications such as autonomous driving. Consequently, it is of great significance to ensure t…
Robust Average-Reward Markov Decision Processes
Yue Wang, Alvaro Velasquez, George Atia +2
In robust Markov decision processes (MDPs), the uncertainty in the transition kernel is addressed by finding a policy that optimizes the worst-case performance over an uncertainty…
On the Robustness of AlphaFold: A COVID-19 Case Study
Ismail Alkhouri, Sumit Jha, Andre Beckus +5
Protein folding neural networks (PFNNs) such as AlphaFold predict remarkably accurate structures of proteins compared to other approaches. However, the robustness of such networks…