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Where do Models go Wrong? Parameter-Space Saliency Maps for Explainability., , , , , and . CoRR, (2021)Battle of the Backbones: A Large-Scale Comparison of Pretrained Models across Computer Vision Tasks., , , , , , , , , and 3 other author(s). CoRR, (2023)Encoding Robustness to Image Style via Adversarial Feature Perturbations., , , and . NeurIPS, page 28042-28053. (2021)Coercing LLMs to do and reveal (almost) anything., , , , , and . CoRR, (2024)Improving Robustness of Learning-based Autonomous Steering Using Adversarial Images., , , , , and . CoRR, (2021)Bring Your Own Data! Self-Supervised Evaluation for Large Language Models., , , , , , , , and . CoRR, (2023)Where do Models go Wrong? Parameter-Space Saliency Maps for Explainability., , , , , and . NeurIPS, (2022)Prepare for the Worst: Generalizing across Domain Shifts with Adversarial Batch Normalization., , , and . CoRR, (2020)Shadowcast: Stealthy Data Poisoning Attacks Against Vision-Language Models., , , , , , , and . CoRR, (2024)On the Reliability of Watermarks for Large Language Models., , , , , , , , , and . CoRR, (2023)