Modern language models (LMs) have been successfully employed in source code
generation and understanding, leading to a significant increase in research focused on …

With the widespread application in industrial manufacturing and commercial services, well-
trained deep neural networks (DNNs) are becoming increasingly valuable and crucial …

The commercial use of machine learning (ML) is spreading; at the same time, ML models
are becoming more complex and more expensive to train, which makes intellectual property …

Recently non-transferable learning (NTL) was proposed to restrict models' generalization
toward the target domain (s) which serves as state-of-the-art solutions for intellectual …

Numerous works study black-box attacks on image classifiers, where adversaries generate
adversarial examples against unknown target models without having access to their internal …

Can we recover the hidden parameters of an Artificial Neural Network (ANN) by probing its
input-output mapping? We propose a systematic method, calledExpand-and-Cluster'that …

Deep learning has shown incredible potential across a vast array of tasks and
accompanying this growth has been an insatiable appetite for data. However, a large …

Counterfactual explanations find ways of achieving a favorable model outcome with
minimum input perturbation. However, counterfactual explanations can also be exploited to …

In contrast to vast academic efforts to study AI security, few real-world reports of AI security
incidents exist. Released incidents prevent a thorough investigation of the attackers' …

Inference is a significant part of ML software infrastructure. Despite the variety of inference
frameworks available, the field as a whole can be considered in its early days. This position …