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Physical Consistency Bridges Heterogeneous Data in Molecular Multi-Task Learning
To support various molecular properties at scale, machine learning models are trained in the multi-task learning paradigm.
Neural P$^3$M: A Long-Range Interaction Modeling Enhancer for Geometric GNNs
Geometric graph neural networks (GNNs) have emerged as powerful tools for modeling molecular geometry.
QCQP-Net: Reliably Learning Feasible Alternating Current Optimal Power Flow Solutions Under Constraints
At the heart of power system operations, alternating current optimal power flow (ACOPF) studies the generation of electric power in the most economical way under network-wide load requirement, and can be formulated as a highly structured non-convex quadratically constrained quadratic program (QCQP).
Bayesian Risk Markov Decision Processes
We consider finite-horizon Markov Decision Processes where parameters, such as transition probabilities, are unknown and estimated from data.
