VentureBeat

When is ART useful? When it’s IBM’s Adversarial Robustness Toolbox for AI

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More IBM is hoping to advance the state of the art for artificial intelligence ...
EurekAlert!

Streaming algorithms for triangle counting: adversarial robustness and the weighted case

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.
EurekAlert!

A prompt-based approach to adversarial example generation and robustness enhancement

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 ...
Semiconductor Engineering

Multiexpert Adversarial Regularization For Robust And Data-Efficient Deep Supervised Learning

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 ...
Harvard Business School

Evaluating Adversarial Robustness of Concept Representations in Sparse Autoencoders

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 ...
VentureBeat

When is ART useful? When it's IBM's Adversarial Robustness Toolbox for AI

IBM is hoping to advance the state of the art for artificial intelligence (AI) security with an open source project called the Adversarial Robustness Toolbox (ART). As AI usage is growing rapidly, ...