7 citations · 19 across the 10 of their papers we have counts for
4 papers · 1 filter
Localized LoRA-MoE: Block-wise Low-Rank Experts With Adaptive Routing
Babak Barazandeh, Subhabrata Majumdar, Vinay Prithyani +1
Large Language Models (LLMs) and high-dimensional perception networks increasingly rely on parameter-efficient fine-tuning (PEFT) to adapt to diverse operational contexts. However,…
Localized LoRA: A Structured Low-Rank Approximation for Efficient Fine-Tuning
Babak Barazandeh, Subhabrata Majumdar, Om Rajyaguru +1
Parameter-efficient fine-tuning (PEFT) methods, such as LoRA, offer compact and effective alternatives to full model fine-tuning by introducing low-rank updates to pre-trained weig…
Training generative networks using random discriminators
Babak Barazandeh, Meisam Razaviyayn, Maziar Sanjabi
In recent years, Generative Adversarial Networks (GANs) have drawn a lot of attentions for learning the underlying distribution of data in various applications. Despite their wide…
On the Behavior of the Expectation-Maximization Algorithm for Mixture Models
Babak Barazandeh, Meisam Razaviyayn
Finite mixture models are among the most popular statistical models used in different data science disciplines. Despite their broad applicability, inference under these models typi…