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Found 6 papers, 4 papers with code
Simulation-guided Beam Search for Neural Combinatorial Optimization
Neural approaches for combinatorial optimization (CO) equip a learning mechanism to discover powerful heuristics for solving complex real-world problems.
Matrix Encoding Networks for Neural Combinatorial Optimization
A popular approach is to use a neural net to compute on the parameters of a given CO problem and extract useful information that guides the search for good solutions.
Efficient Active Search for Combinatorial Optimization Problems
While active search is simple to implement, it is not competitive with state-of-the-art methods because adjusting all model weights for each test instance is very time and memory intensive.
SelfMatch: Combining Contrastive Self-Supervision and Consistency for Semi-Supervised Learning
This paper introduces SelfMatch, a semi-supervised learning method that combines the power of contrastive self-supervised learning and consistency regularization.
POMO: Policy Optimization with Multiple Optima for Reinforcement Learning
We introduce Policy Optimization with Multiple Optima (POMO), an end-to-end approach for building such a heuristic solver.
Femtojoule-scale all-optical latching and modulation via cavity nonlinear optics
The data are analyzed using a semiclassical model that explicitly treats heterogeneous coupling of atoms to the cavity mode.
Optics Atomic Physics Quantum Physics
