6 papers
Reinforcement Learning for Graph Generation under a Hard Assortativity Constraint
Hoyun Choi, Junghyo Jo, Deok-Sun Lee
Generating graph ensembles with precisely controlled structural properties is central to investigating how network structure shapes function. Canonical ensembles impose constraints…
Variational Garrote for Sparse Inverse Problems
Kanghun Lee, Hyungjoon Soh, Junghyo Jo
Sparse regularization plays a central role in solving inverse problems arising from incomplete or corrupted measurements. Different regularizers correspond to different prior assum…
Stochastic Clock Attention for Aligning Continuous and Ordered Sequences
Hyungjoon Soh, Junghyo Jo
We formulate an attention mechanism for continuous and ordered sequences that explicitly functions as an alignment model, which serves as the core of many sequence-to-sequence task…
Data augmentation using diffusion models to enhance inverse Ising inference
Yechan Lim, Sangwon Lee, Junghyo Jo
Identifying model parameters from observed configurations poses a fundamental challenge in data science, especially with limited data. Recently, diffusion models have emerged as a…
Neural Graph Simulator for Complex Systems
Hoyun Choi, Sungyeop Lee, B. Kahng +1
Numerical simulation is a predominant tool for studying the dynamics in complex systems, but large-scale simulations are often intractable due to computational limitations. Here, w…
GNRK: Graph Neural Runge-Kutta method for solving partial differential equations
Hoyun Choi, Sungyeop Lee, B. Kahng +1
Neural networks have proven to be efficient surrogate models for tackling partial differential equations (PDEs). However, their applicability is often confined to specific PDEs und…