127 citations · 308 across the 18 of their papers we have counts for
4 papers · 1 filter
Are Modern Speech Enhancement Systems Vulnerable to Adversarial Attacks?
Rostislav Makarov, Lea Schönherr, Timo Gerkmann
Machine learning approaches for speech enhancement are becoming increasingly expressive, enabling ever more powerful modifications of input signals. In this paper, we demonstrate t…
Trust Me, I Know This Function: Hijacking LLM Static Analysis using Bias
Shir Bernstein, David Beste, Daniel Ayzenshteyn +2
Large Language Models (LLMs) are increasingly trusted to perform automated code review and static analysis at scale, supporting tasks such as vulnerability detection, summarization…
Buffer-free Class-Incremental Learning with Out-of-Distribution Detection
Srishti Gupta, Daniele Angioni, Maura Pintor +4
Class-incremental learning (CIL) poses significant challenges in open-world scenarios, where models must not only learn new classes over time without forgetting previous ones but a…
Rethinking Robustness in Machine Learning: A Posterior Agreement Approach
João Borges S. Carvalho, Victor Jimenez Rodriguez, Alessandro Torcinovich +4
The robustness of algorithms against covariate shifts is a fundamental problem with critical implications for the deployment of machine learning algorithms in the real world. Curre…