1 citations · 2 across the 7 of their papers we have counts for
18 papers
Provable Speech Attributes Conversion via Latent Independence
Jonathan Svirsky, Ofir Lindenbaum, Uri Shaham
While signal conversion and disentangled representation learning have shown promise for manipulating data attributes across domains such as audio, image, and multimodal generation,…
TempoControl: Temporal Attention Guidance for Text-to-Video Models
Shira Schiber, Ofir Lindenbaum, Idan Schwartz
Recent advances in generative video models have enabled the creation of high-quality videos based on natural language prompts. However, these models frequently lack fine-grained te…
LORENZA: Enhancing Generalization in Low-Rank Gradient LLM Training via Efficient Zeroth-Order Adaptive SAM
Yehonathan Refael, Iftach Arbel, Ofir Lindenbaum +1
We study robust parameter-efficient fine-tuning (PEFT) techniques designed to improve accuracy and generalization while operating within strict computational and memory hardware co…
FineGates: LLMs Finetuning with Compression using Stochastic Gates
Jonathan Svirsky, Yehonathan Refael, Ofir Lindenbaum
Large Language Models (LLMs), with billions of parameters, present significant challenges for full finetuning due to the high computational demands, memory requirements, and imprac…
Generalizable and Robust Spectral Method for Multi-view Representation Learning
Amitai Yacobi, Ofir Lindenbaum, Uri Shaham
Multi-view representation learning (MvRL) has garnered substantial attention in recent years, driven by the increasing demand for applications that can effectively process and anal…
TransformLLM: Adapting Large Language Models via LLM-Transformed Reading Comprehension Text
Iftach Arbel, Yehonathan Refael, Ofir Lindenbaum
Large Language Models (LLMs) have shown promise in highly-specialized domains, however challenges are still present in aspects of accuracy and costs. These limitations restrict the…