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Deep neural networks (DNNs) are demonstrated to be vulnerable to adversarial examples. Adversarial training is mainstrem ...
Estimating the number of triangles in a graph is a fundamental problem and has found applications in many fields. This problem has been widely studied in the context of graph stream processing.
Most artificial intelligence researchers agree that one of the key concerns of machine learning is adversarial attacks, data manipulation techniques that cause trained models to behave in undesired ...
Recent years have seen the wide application of NLP models in crucial areas such as finance, medical treatment, and news media, raising concerns about the model robustness. Existing methods are mainly ...
Deep neural networks (DNNs) can achieve high accuracy when there is abundant training data that has the same distribution as the test data. In practical applications, data deficiency is often a ...
Jiaxun Li, Aaron, Suraj Srinivas, Usha Bhalla, and Himabindu Lakkaraju. "Evaluating Adversarial Robustness of Concept Representations in Sparse Autoencoders." Proceedings of the Conference of the ...
GRASP is a new gradient-based planner for learned dynamics (a “world model”) that makes long-horizon planning practical by (1 ...
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