4 papers
Linearization Explains Fine-Tuning in Large Language Models
Zahra Rahimi Afzal, Tara Esmaeilbeig, Mojtaba Soltanalian +1
Parameter-Efficient Fine-Tuning (PEFT) is a popular class of techniques that strive to adapt large models in a scalable and resource-efficient manner. Yet, the mechanisms underlyin…
RoCoFT: Efficient Finetuning of Large Language Models with Row-Column Updates
Md Kowsher, Tara Esmaeilbeig, Chun-Nam Yu +3
We propose RoCoFT, a parameter-efficient fine-tuning method for large-scale language models (LMs) based on updating only a few rows and columns of the weight matrices in transforme…
Deep Learning-Enabled One-Bit DoA Estimation
Farhang Yeganegi, Arian Eamaz, Tara Esmaeilbeig +1
Unrolled deep neural networks have attracted significant attention for their success in various practical applications. In this paper, we explore an application of deep unrolling i…
Beyond Diagonal RIS: Key to Next-Generation Integrated Sensing and Communications?
Tara Esmaeilbeig, Kumar Vijay Mishra, Mojtaba Soltanalian
Reconfigurable intelligent surface (RIS) have introduced unprecedented flexibility and adaptability toward smart wireless channels. Recent research on integrated sensing and commun…