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Enhancing Adversarial Robustness via Score-Based Optimization
Adversarial attacks have the potential to mislead deep neural network classifiers by introducing slight perturbations.
Benchmarking Robustness of Adaptation Methods on Pre-trained Vision-Language Models (Supplementary) Shuo Chen 1,3 Jindong Gu2 Zhen Han 1 Y unpu Ma1,3
BART had higher robustness against text corruptions on the GQA dataset. This may be due to the different language encoders used in BART and T5.Among all adaptation The top row shows the robustness against image corruptions and the bottom row is results against text corruptions. We also choose different embedding dimensions for adapter-based methods. The relative robustness of adaptation methods based on CLIP-T5 is presented in Table 1. Figure 2
On Dynamic Programming Decompositions of Static Risk Measures in Markov Decision Processes
Risk-averse reinforcement learning (RL) seeks to provide a risk-averse policy for high-stakes real-world decision problems. These high-stake domains include autonomous driving (Jin et al., 2019; Sharma et al., 2020), robot collision avoidance (Ahmadi et al., 2021; Hakobyan and Y ang, 2021),
Content-based Unrestricted Adversarial Attack
Unrestricted adversarial attacks typically manipulate the semantic content of an image ( e.g., color or texture) to create adversarial examples that are both effective and photorealistic, demonstrating their ability to deceive human perception
Even Sparser Graph Transformers
As the learned attention mechanisms tend to use few of these edges, such high-degree connections may be unnecessary.