Systems Intelligence Laboratory seeks to develop data-driven approaches to analyzing, monitoring, and controlling complex engineering systems, with an attempt to optimize designs, managements, and operations of various target systems.
Principal Investigator: Prof. Jinkyoo Park
Website: http://silab.kaist.ac.kr
[NeurIPS 23] Official Code for "Learning Efficient Surrogate Dynamic Models with Graph Spline Networks"
[AAMAS 2024] HiMAP: Learning Heuristics-Informed Policies for Large-Scale Multi-Agent Pathfinding
[ICML 2023] Meta-SAGE: Scale Meta-Learning Scheduled Adaptation with Guided Exploration for Mitigating Scale Shift on Combinatorial Optimization
[NeurIPS 23] Official code for BootGen: "Bootstrapped Training of Score Conditioned Generator for Offline Biological Design"
Baselines for Model-Based Optimization installation fixes and compatible with newer AMPERE+ GPUs (e.g. 3090)
Collection of resources about partial differential equations, graph neural networks, deep learning and dynamical system simulation
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