22 citations · 24 across the 7 of their papers we have counts for
5 papers · 1 filter
Watch your neighbors: Training statistically accurate chaotic systems with local phase space information
Joon-Hyuk Ko, Andrus Giraldo, Deok-Sun Lee
Chaotic systems pose fundamental challenges for data-driven dynamics discovery, as small modeling errors lead to exponentially growing trajectory discrepancies. Since exact long-te…
A Robust In-Context Model for Conservation Laws: Injecting Context into Flux Neural Operators via Recurrent Vision Transformers
Taeyoung Kim, Joon-Hyuk Ko
We propose an architecture that augments the Flux Neural Operator (Flux NO), which combines the classical finite volume method (FVM) with neural operators, with ViT-based context i…
Moderate Adaptive Linear Units (MoLU)
Hankyul Koh, Joon-hyuk Ko, Wonho Jhe
We propose the Moderate Adaptive Linear Unit (MoLU), a novel activation function for deep neural networks, defined analytically as: f(x)=x \times (1+tanh(x))/2. MoLU combines mathe…
Homotopy-based training of NeuralODEs for accurate dynamics discovery
Joon-Hyuk Ko, Hankyul Koh, Nojun Park +1
Neural Ordinary Differential Equations (NeuralODEs) present an attractive way to extract dynamical laws from time series data, as they bridge neural networks with the differential…
Bayesian neural network with pretrained protein embedding enhances prediction accuracy of drug-protein interaction
QHwan Kim, Joon-Hyuk Ko, Sunghoon Kim +2
The characterization of drug-protein interactions is crucial in the high-throughput screening for drug discovery. The deep learning-based approaches have attracted attention becaus…