LeapfrogLayers: A Trainable Framework for Effective Topological Sampling
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Abstract (arXiv)
We introduce LeapfrogLayers, an invertible neural network architecture that can be trained to efficiently sample the topology of a 2D U(1) lattice gauge theory. We show an improvement in the integrated autocorrelation time of the topological charge when compared with traditional HMC, and look at how different quantities transform under our model. Our implementation is open source, and is publicly available on github at https://github.com/saforem2/l2hmc-qcd.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 10 of its 20 references that are in the map. First posted on arXiv 2 Dec 2021.
Builds on
10 of 20 references.
References · 10
- 2016Density estimation using Real NVPcited by 2,029
- 2020Equivariant flow-based sampling for lattice gauge theorycited by 102
- 2017Generalizing Hamiltonian Monte Carlo with Neural Networkscited by 52
- 2018Neural Network Renormalization Groupcited by 48
- 2021Introduction to Normalizing Flows for Lattice Field Theorycited by 35
- 2020Involutive MCMC: a Unifying Frameworkcited by 19
- 2020A Neural Network MCMC sampler that maximizes Proposal Entropycited by 12
- 2021Deep Learning Hamiltonian Monte Carlocited by 7
- 2020You say Normalizing Flows I see Bayesian Networkscited by 5
- 2020Orbital MCMCcited by 5
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Citing papers per year: 2022: 2; 2023: 0; 2024: 1.
3 papers.