# Unbiased Monte Carlo Cluster Updates with Autoregressive Neural Networks

@article{Wu2021UnbiasedMC, title={Unbiased Monte Carlo Cluster Updates with Autoregressive Neural Networks}, author={Dian Wu and Riccardo Rossi and Giuseppe Carleo}, journal={ArXiv}, year={2021}, volume={abs/2105.05650} }

Efficient sampling of complex high-dimensional probability densities is a central task in computational science. Machine Learning techniques based on autoregressive neural networks have been recently shown to provide good approximations of probability distributions of interest in physics. In this work, we propose a systematic way to remove the intrinsic bias associated with these variational approximations, combining it with Markov-chain Monte Carlo in an automatic scheme to efficiently… Expand

#### 3 Citations

Analysis of autocorrelation times in Neural Markov Chain Monte Carlo simulations

- Computer Science, Physics
- ArXiv
- 2021

A deepened study of autocorrelations in Neural Markov Chain Monte Carlo simulations, a version of the traditional Metropolis algorithm which employs neural networks to provide independent proposals, and a scheme which incorporates partial heat-bath updates is proposed. Expand

Sampling Lattices in Semi-Grand Canonical Ensemble with Autoregressive Machine Learning

- Physics
- 2021

Calculating thermodynamic potentials and observables efficiently and accurately is key for the application of statistical mechanics simulations to materials science. However, naive Monte Carlo… Expand

Efficient modeling of trivializing maps for lattice
ϕ4
theory using normalizing flows: A first look at scalability

- Physics
- Physical Review D
- 2021

General-purpose Markov Chain Monte Carlo sampling algorithms suffer from a dramatic reduction in efficiency as the system being studied is driven towards a critical point through, for example, taking… Expand

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