5 citations · 8 across the 2 of their papers we have counts for
6 papers
Orak: A Foundational Benchmark for Training and Evaluating LLM Agents on Diverse Video Games
Dongmin Park, Minkyu Kim, Beongjun Choi +13
Large Language Model (LLM) agents are reshaping the game industry, by enabling more intelligent and human-preferable characters. Yet, current game benchmarks fall short of practica…
Modeling and simulations of high-density two-phase flows using projection-based Cahn-Hilliard Navier-Stokes equations
Ali Rabeh, Makrand A. Khanwale, John J. Lee +1
Accurately modeling the dynamics of high-density ratio () two-phase flows is important for many material science and manufacturing applications. This work consid…
SF(DA): Source-free Domain Adaptation Through the Lens of Data Augmentation
Uiwon Hwang, Jonghyun Lee, Juhyeon Shin +1
In the face of the deep learning model's vulnerability to domain shift, source-free domain adaptation (SFDA) methods have been proposed to adapt models to new, unseen target domain…
Entropy is not Enough for Test-Time Adaptation: From the Perspective of Disentangled Factors
Jonghyun Lee, Dahuin Jung, Saehyung Lee +4
Test-time adaptation (TTA) fine-tunes pre-trained deep neural networks for unseen test data. The primary challenge of TTA is limited access to the entire test dataset during online…
Efficient Diffusion-Driven Corruption Editor for Test-Time Adaptation
Yeongtak Oh, Jonghyun Lee, Jooyoung Choi +3
Test-time adaptation (TTA) addresses the unforeseen distribution shifts occurring during test time. In TTA, performance, memory consumption, and time consumption are crucial consid…
STAG: Structural Test-time Alignment of Gradients for Online Adaptation
Juhyeon Shin, Yujin Oh, Jonghyun Lee +5
Test-Time Adaptation (TTA) adapts pre-trained models using only unlabeled test streams, requiring real-time inference and update without access to source data. We propose Structura…