activity
20232025
most citedThe Expressive Power of Low-Rank Adaptation

5 citations · 9 across the 7 of their papers we have counts for

collaborators

7 papers

cs.LG2025

In-Context Learning with Hypothesis-Class Guidance

Ziqian Lin, Shubham Kumar Bharti, Kangwook Lee

Recent research has investigated the underlying mechanisms of in-context learning (ICL) both theoretically and empirically, often using data generated from simple function classes.…

eess.IV2024★ 1 cited

HCL-Diff: Hybrid conditional latent diffusion with high frequency enhancement for CBCT-to-CT synthesis

Shi Yin, Hongqi Tan, Li Ming Chong +6

Background: Cone-beam computed tomography (CBCT) plays a crucial role in image-guided radiotherapy, but artifacts and noise make them unsuitable for accurate dose calculation. Arti…

cs.LG2024

Parameter-Efficient Fine-Tuning of State Space Models

Kevin Galim, Wonjun Kang, Yuchen Zeng +2

Deep State Space Models (SSMs), such as Mamba (Gu & Dao, 2024), have become powerful tools for language modeling, offering high performance and linear scalability with sequence len…

cs.LG2024★ 1 cited

Dual Operating Modes of In-Context Learning

Ziqian Lin, Kangwook Lee

In-context learning (ICL) exhibits dual operating modes: task learning, i.e., acquiring a new skill from in-context samples, and task retrieval, i.e., locating and activating a rel…

cs.LG2024

Can MLLMs Perform Text-to-Image In-Context Learning?

Yuchen Zeng, Wonjun Kang, Yicong Chen +2

The evolution from Large Language Models (LLMs) to Multimodal Large Language Models (MLLMs) has spurred research into extending In-Context Learning (ICL) to its multimodal counterp…

astro-ph.CO2023★ 2 cited

Super-Resolution Emulation of Large Cosmological Fields with a 3D Conditional Diffusion Model

Adam Rouhiainen, Michael Gira, Moritz Münchmeyer +2

High-resolution (HR) simulations in cosmology, in particular when including baryons, can take millions of CPU hours. On the other hand, low-resolution (LR) dark matter simulations…

cs.LG2023★ 5 cited

The Expressive Power of Low-Rank Adaptation

Yuchen Zeng, Kangwook Lee

Low-Rank Adaptation (LoRA), a parameter-efficient fine-tuning method that leverages low-rank adaptation of weight matrices, has emerged as a prevalent technique for fine-tuning pre…