6 papers
Active Learning with Selective Time-Step Acquisition for PDEs
Yegon Kim, Hyunsu Kim, Gyeonghoon Ko +1
Accurately solving partial differential equations (PDEs) is critical to understanding complex scientific and engineering phenomena, yet traditional numerical solvers are computatio…
Axial Neural Networks for Dimension-Free Foundation Models
Hyunsu Kim, Jonggeon Park, Joan Bruna +2
The advent of foundation models in AI has significantly advanced general-purpose learning, enabling remarkable capabilities in zero-shot inference and in-context learning. However,…
Parameter Expanded Stochastic Gradient Markov Chain Monte Carlo
Hyunsu Kim, Giung Nam, Chulhee Yun +2
Bayesian Neural Networks (BNNs) provide a promising framework for modeling predictive uncertainty and enhancing out-of-distribution robustness (OOD) by estimating the posterior dis…
Learning Infinitesimal Generators of Continuous Symmetries from Data
Gyeonghoon Ko, Hyunsu Kim, Juho Lee
Exploiting symmetry inherent in data can significantly improve the sample efficiency of a learning procedure and the generalization of learned models. When data clearly reveals und…
Variational Partial Group Convolutions for Input-Aware Partial Equivariance of Rotations and Color-Shifts
Hyunsu Kim, Yegon Kim, Hongseok Yang +1
Group Equivariant CNNs (G-CNNs) have shown promising efficacy in various tasks, owing to their ability to capture hierarchical features in an equivariant manner. However, their equ…
Fast Ensembling with Diffusion Schrödinger Bridge
Hyunsu Kim, Jongmin Yoon, Juho Lee
Deep Ensemble (DE) approach is a straightforward technique used to enhance the performance of deep neural networks by training them from different initial points, converging toward…