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Found 8 papers, 6 papers with code
Attention, Learn to Solve Routing Problems!
The recently presented idea to learn heuristics for combinatorial optimization problems is promising as it can save costly development.
Stochastic Beams and Where to Find Them: The Gumbel-Top-k Trick for Sampling Sequences Without Replacement
We show how to implicitly apply this 'Gumbel-Top-$k$' trick on a factorized distribution over sequences, allowing to draw exact samples without replacement using a Stochastic Beam Search.
Buy 4 REINFORCE Samples, Get a Baseline for Free!
REINFORCE can be used to train models in structured prediction settings to directly optimize the test-time objective.
Estimating Gradients for Discrete Random Variables by Sampling without Replacement
We derive an unbiased estimator for expectations over discrete random variables based on sampling without replacement, which reduces variance as it avoids duplicate samples.
Deep Policy Dynamic Programming for Vehicle Routing Problems
Routing problems are a class of combinatorial problems with many practical applications.
Unbiased Gradient Estimation with Balanced Assignments for Mixtures of Experts
Training large-scale mixture of experts models efficiently on modern hardware requires assigning datapoints in a batch to different experts, each with a limited capacity.
A Review of the Gumbel-max Trick and its Extensions for Discrete Stochasticity in Machine Learning
The Gumbel-max trick is a method to draw a sample from a categorical distribution, given by its unnormalized (log-)probabilities.
PyVRP: a high-performance VRP solver package
We hope that PyVRP enables researchers and practitioners to easily and quickly build on a state-of-the-art VRP solver.
