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Found 7 papers, 5 papers with code
Moco: A Learnable Meta Optimizer for Combinatorial Optimization
Our approach, Moco, learns a graph neural network that updates the solution construction procedure based on features extracted from the current search state.
Routing Arena: A Benchmark Suite for Neural Routing Solvers
Neural Combinatorial Optimization has been researched actively in the last eight years.
DeepStay: Stay Region Extraction from Location Trajectories using Weak Supervision
Common approaches to SR extraction are evaluated either solely unsupervised or on a small-scale private dataset, as popular public datasets are unlabeled.
Learning to Control Local Search for Combinatorial Optimization
Combinatorial optimization problems are encountered in many practical contexts such as logistics and production, but exact solutions are particularly difficult to find and usually NP-hard for considerable problem sizes.
Large Neighborhood Search based on Neural Construction Heuristics
The neural repair operator is combined with a local search routine, heuristic destruction operators and a selection procedure applied to a small population to arrive at a sophisticated solution approach.
Supervised Permutation Invariant Networks for Solving the CVRP with Bounded Fleet Size
Learning to solve combinatorial optimization problems, such as the vehicle routing problem, offers great computational advantages over classical operations research solvers and heuristics.
Do We Really Need Deep Learning Models for Time Series Forecasting?
In this paper, we report the results of prominent deep learning models with respect to a well-known machine learning baseline, a Gradient Boosting Regression Tree (GBRT) model.
