80 citations · 271 across the 17 of their papers we have counts for
18 papers
Hydra: Multi-head Low-rank Adaptation for Parameter Efficient Fine-tuning
Sanghyeon Kim, Hyunmo Yang, Younghyun Kim +2
The recent surge in large-scale foundation models has spurred the development of efficient methods for adapting these models to various downstream tasks. Low-rank adaptation method…
Separable Physics-Informed Neural Networks
Junwoo Cho, Seungtae Nam, Hyunmo Yang +3
Physics-informed neural networks (PINNs) have recently emerged as promising data-driven PDE solvers showing encouraging results on various PDEs. However, there is a fundamental lim…
SMPConv: Self-moving Point Representations for Continuous Convolution
Sanghyeon Kim, Eunbyung Park
Continuous convolution has recently gained prominence due to its ability to handle irregularly sampled data and model long-term dependency. Also, the promising experimental results…
FFNeRV: Flow-Guided Frame-Wise Neural Representations for Videos
Joo Chan Lee, Daniel Rho, Jong Hwan Ko +1
Neural fields, also known as coordinate-based or implicit neural representations, have shown a remarkable capability of representing, generating, and manipulating various forms of…
Masked Wavelet Representation for Compact Neural Radiance Fields
Daniel Rho, Byeonghyeon Lee, Seungtae Nam +3
Neural radiance fields (NeRF) have demonstrated the potential of coordinate-based neural representation (neural fields or implicit neural representation) in neural rendering. Howev…
Separable PINN: Mitigating the Curse of Dimensionality in Physics-Informed Neural Networks
Junwoo Cho, Seungtae Nam, Hyunmo Yang +3
Physics-informed neural networks (PINNs) have emerged as new data-driven PDE solvers for both forward and inverse problems. While promising, the expensive computational costs to ob…