1 citations · 2 across the 5 of their papers we have counts for
12 papers
LiME: Lightweight Mixture of Experts for Efficient Multimodal Multi-task Learning
Md Kowsher, Haris Mansoor, Nusrat Jahan Prottasha +4
MoE-PEFT methods combine Mixture of Experts with parameter-efficient fine-tuning for multi-task adaptation, but require separate adapters per expert causing trainable parameters to…
Monkey Jump : MoE-Style PEFT for Efficient Multi-Task Learning
Nusrat Jahan Prottasha, Md Kowsher, Chun-Nam Yu +2
Mixture-of-experts variants of parameter-efficient fine-tuning enable per-token specialization, but they introduce additional trainable routers and expert parameters, increasing me…
FlowNIB: An Information Bottleneck Analysis of Bidirectional vs. Unidirectional Language Models
Md Kowsher, Nusrat Jahan Prottasha, Shiyun Xu +4
Bidirectional language models have better context understanding and perform better than unidirectional models on natural language understanding tasks, yet the theoretical reasons b…
PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models
Nusrat Jahan Prottasha, Upama Roy Chowdhury, Shetu Mohanto +7
Large models such as Large Language Models (LLMs) and Vision Language Models (VLMs) have transformed artificial intelligence, powering applications in natural language processing,…
User Profile with Large Language Models: Construction, Updating, and Benchmarking
Nusrat Jahan Prottasha, Md Kowsher, Hafijur Raman +4
User profile modeling plays a key role in personalized systems, as it requires building accurate profiles and updating them with new information. In this paper, we present two high…
BnTTS: Few-Shot Speaker Adaptation in Low-Resource Setting
Mohammad Jahid Ibna Basher, Md Kowsher, Md Saiful Islam +8
This paper introduces BnTTS (Bangla Text-To-Speech), the first framework for Bangla speaker adaptation-based TTS, designed to bridge the gap in Bangla speech synthesis using minima…