43 citations · 95 across the 13 of their papers we have counts for
20 papers
Learning context-aware adaptive solvers to accelerate quadratic programming
Haewon Jung, Junyoung Park, Jinkyoo Park
Convex quadratic programming (QP) is an important sub-field of mathematical optimization. The alternating direction method of multipliers (ADMM) is a successful method to solve QP.…
Bayesian Convolutional Deep Sets with Task-Dependent Stationary Prior
Yohan Jung, Jinkyoo Park
Convolutional deep sets are the architecture of a deep neural network (DNN) that can model stationary stochastic process. This architecture uses the kernel smoother and the DNN to…
Neural Solvers for Fast and Accurate Numerical Optimal Control
Federico Berto, Stefano Massaroli, Michael Poli +1
Synthesizing optimal controllers for dynamical systems often involves solving optimization problems with hard real-time constraints. These constraints determine the class of numeri…
Learning Collaborative Policies to Solve NP-hard Routing Problems
Minsu Kim, Jinkyoo Park, Joungho Kim
Recently, deep reinforcement learning (DRL) frameworks have shown potential for solving NP-hard routing problems such as the traveling salesman problem (TSP) without problem-specif…
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…
Neural Hybrid Automata: Learning Dynamics with Multiple Modes and Stochastic Transitions
Michael Poli, Stefano Massaroli, Luca Scimeca +6
Effective control and prediction of dynamical systems often require appropriate handling of continuous-time and discrete, event-triggered processes. Stochastic hybrid systems (SHSs…