3 papers
cs.CL2026
Small Data, Big Noise: Adversarial Training for Robust Parameter-Efficient Fine-Tuning
Eitan Cohen, Idan Simai, Uri Shaham
Parameter-Efficient Fine-Tuning (PEFT) has become essential for adapting foundation models to downstream NLP tasks. However, current PEFT methods often struggle with robustness to…
cs.CV2026
RealStats: A Rigorous Real-Only Statistical Framework for Fake Image Detection
Haim Zisman, Uri Shaham
As generative models continue to evolve, detecting AI-generated images remains a critical challenge. While effective detection methods exist, they often lack formal interpretabilit…
eess.AS2024
Sparse Binarization for Fast Keyword Spotting
Jonathan Svirsky, Uri Shaham, Ofir Lindenbaum
With the increasing prevalence of voice-activated devices and applications, keyword spotting (KWS) models enable users to interact with technology hands-free, enhancing convenience…