2 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.CV2025
Enhancing VICReg: Random-Walk Pairing for Improved Generalization and Better Global Semantics Capturing
Idan Simai, Ronen Talmon, Uri Shaham
In this paper, we argue that viewing VICReg-a popular self-supervised learning (SSL) method--through the lens of spectral embedding reveals a potential source of sub-optimality: it…