331 citations · 468 across the 14 of their papers we have counts for
5 papers · 1 filter
Continuous-Depth Neural Models for Dynamic Graph Prediction
Michael Poli, Stefano Massaroli, Clayton M. Rabideau +4
We introduce the framework of continuous-depth graph neural networks (GNNs). Neural graph differential equations (Neural GDEs) are formalized as the counterpart to GNNs where the i…
ScheduleNet: Learn to solve multi-agent scheduling problems with reinforcement learning
Junyoung Park, Sanjar Bakhtiyar, Jinkyoo Park
We propose ScheduleNet, a RL-based real-time scheduler, that can solve various types of multi-agent scheduling problems. We formulate these problems as a semi-MDP with episodic rew…
Learning to schedule job-shop problems: Representation and policy learning using graph neural network and reinforcement learning
Junyoung Park, Jaehyeong Chun, Sang Hun Kim +2
We propose a framework to learn to schedule a job-shop problem (JSSP) using a graph neural network (GNN) and reinforcement learning (RL). We formulate the scheduling process of JSS…
Convergent Graph Solvers
Junyoung Park, Jinhyun Choo, Jinkyoo Park
We propose the convergent graph solver (CGS), a deep learning method that learns iterative mappings to predict the properties of a graph system at its stationary state (fixed point…
A Molecular Hyper-message Passing Network with Functional Group Information
Fangying Chen, Junyoung Park, Jinkyoo Park
We proposed the molecular hyper-message passing network (MolHMPN) that predicts the properties of a molecule with prior knowledge-guided subgraph. Modeling higher-order connectivit…