Chin-Lun (Allen) Fu

I am Chin-Lun (Allen) Fu, and I am a second-year MSCS student at UCLA. My researh focused on multi-modal learning and instrcution fine-tuning. In the past, I received a B.S. degree from the Electrical Engineering Department at National Taiwan University, where my research focuses on parameter-efficient-tuning with Large Language Models (LLMs) in different domains, spanning from Natural Language Processing (NLP), Speech, and Computer Vision (CV).

At National Taiwan University, I was fortunate to work with Prof. Hung-yi Lee on efficient-tuning in NLP and Speech tasks, and with Prof. Yu-Chiang Frank Wang on domain generalized problem.

Email  /  CV  /  Google Scholar  /  Github  /  LinkedIn

J.P.Morgan MLCOE

[2024.06 - 2024.09]

AI/ML Associate

Microsoft AI

[2022.04 - 2022.11]

Research Intern

In this work, we present AdapterBias. By adding token-dependent representation shifts to the PLM, AdapterBias shows competitive results even though it uses far fewer parameters than the existing methods.

In this study, we aim to explore efficient tuning methods for speech self-supervised learning. We show that the performance parity can be achieved with over 90% parameter reduction, and discussed the pros and cons of efficient tuning techniques. This is the first comprehensive investigation of various adapter types across speech tasks.

Based on the idea of representation disentanglement, we present a network architecture that is able to extract facial liveness, content, and domain features.

Source code credit to Dr. Jon Barron