mlsanityData to 7 Oct 2026

Deep Learning Hamiltonian Monte Carlo

arXiv 2105.03418 ↗First posted 7 May 2021hep-lat cond-mat.stat-mech cs.LGcited by 7

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Abstract (arXiv)

We generalize the Hamiltonian Monte Carlo algorithm with a stack of neural network layers and evaluate its ability to sample from different topologies in a two dimensional lattice gauge theory. We demonstrate that our model is able to successfully mix between modes of different topologies, significantly reducing the computational cost required to generated independent gauge field configurations. Our implementation is available at https://github.com/saforem2/l2hmc-qcd .

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 16 of its 29 references that are in the map. First posted on arXiv 7 May 2021.

Builds on

16 of 29 references.

References · 16

  1. 2016Density estimation using Real NVPcited by 2,029
  2. 2016TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systemscited by 660
  3. 2018Horovod: fast and easy distributed deep learning in TensorFlowcited by 275
  4. 2019Flow-based generative models for Markov chain Monte Carlo in lattice field theorycited by 147
  5. 2020Normalizing Flows on Tori and Spherescited by 117
  6. 2020Equivariant flow-based sampling for lattice gauge theorycited by 102
  7. 2020Sampling using SU(N) gauge equivariant flowscited by 83
  8. 2019NeuTra-lizing Bad Geometry in Hamiltonian Monte Carlo Using Neural Transportcited by 73
  9. 2017Generalizing Hamiltonian Monte Carlo with Neural Networkscited by 52
  10. 2020Lattice gauge equivariant convolutional neural networkscited by 45
  11. 2021Introduction to Normalizing Flows for Lattice Field Theorycited by 35
  12. 2020Involutive MCMC: a Unifying Frameworkcited by 19
4 more
  1. 2020A Neural Network MCMC sampler that maximizes Proposal Entropycited by 12
  2. 2017Towards reduction of autocorrelation in HMC by machine learningcited by 9
  3. 2020You say Normalizing Flows I see Bayesian Networkscited by 5
  4. 2020Orbital MCMCcited by 5

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 2; 2023: 0; 2024: 2; 2025: 0; 2026: 1.

7 papers.

  1. 2022Aspects of scaling and scalability for flow-based sampling of lattice QCDcited by 26
  2. 2022Applications of Machine Learning to Lattice Quantum Field Theorycited by 14
  3. 2024Practical applications of machine-learned flows on gauge fieldscited by 6
  4. 2024Hamiltonian Score Matching and Generative Flowscited by 6
  5. 2021Machine Learning in Nuclear Physicscited by 4
  6. 2021LeapfrogLayers: A Trainable Framework for Effective Topological Samplingcited by 3
  7. 2026Neural Non-Equilibrium Hamiltonian Monte Carlo for Corrected Boltzmann Samplingcited by 0