1 citations · 1 across the 6 of their papers we have counts for
6 papers
Learning When to Adapt
Ali Zindari, Xiaowen Jiang, Rotem Mulayoff +1
Low-rank adaptation (LoRA) is a widely used parameter-efficient fine-tuning method, yet its learned correction is static: the same low-rank update is applied to every input. This i…
LoRA vs. Full Fine-Tuning: A Theoretical Perspective
Ali Zindari, Rotem Mulayoff, Sebastian U. Stich
Fine-tuning adapts a pre-trained model to downstream tasks using a small amount of labeled data. Low-Rank Adaptation (LoRA) is an efficient fine-tuning method that reduces memory a…
Decoupled SGDA for Games with Intermittent Strategy Communication
Ali Zindari, Parham Yazdkhasti, Anton Rodomanov +2
We focus on reducing communication overhead in multiplayer games, where frequently exchanging strategies between players is not feasible and players have noisy or outdated strategi…
The Limits and Potentials of Local SGD for Distributed Heterogeneous Learning with Intermittent Communication
Kumar Kshitij Patel, Margalit Glasgow, Ali Zindari +5
Local SGD is a popular optimization method in distributed learning, often outperforming other algorithms in practice, including mini-batch SGD. Despite this success, theoretically…
Segmentation of Lungs COVID Infected Regions by Attention Mechanism and Synthetic Data
Parham Yazdekhasty, Ali Zindari, Zahra Nabizadeh-ShahreBabak +3
Coronavirus has caused hundreds of thousands of deaths. Fatalities could decrease if every patient could get suitable treatment by the healthcare system. Machine learning, especial…
Bifurcated Autoencoder for Segmentation of COVID-19 Infected Regions in CT Images
Parham Yazdekhasty, Ali Zindar, Zahra Nabizadeh-ShahreBabak +4
The new coronavirus infection has shocked the world since early 2020 with its aggressive outbreak. Rapid detection of the disease saves lives, and relying on medical imaging (Compu…