ATAAT is an adaptive adversarial tuning method that enables effective, stealthy backdoor attacks on VLA models by dynamically selecting gradient decoupling strategies based on attacker capabilities.
International conference on machine learning , pages=
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Different valid temporal partitions of the same streaming dataset can produce materially different rankings and performance numbers for continual learning methods.
citing papers explorer
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ATAAT: Adaptive Threat-Aware Adversarial Tuning Framework against Backdoor Attacks on Vision-Language-Action Models
ATAAT is an adaptive adversarial tuning method that enables effective, stealthy backdoor attacks on VLA models by dynamically selecting gradient decoupling strategies based on attacker capabilities.
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Temporal Taskification in Streaming Continual Learning: A Source of Evaluation Instability
Different valid temporal partitions of the same streaming dataset can produce materially different rankings and performance numbers for continual learning methods.