329 citations · 1.3k across the 48 of their papers we have counts for
55 papers
Training normalizing flows with computationally intensive target probability distributions
Piotr Bialas, Piotr Korcyl, Tomasz Stebel
Machine learning techniques, in particular the so-called normalizing flows, are becoming increasingly popular in the context of Monte Carlo simulations as they can effectively appr…
Mutual information of spin systems from autoregressive neural networks
Piotr Białas, Piotr Korcyl, Tomasz Stebel
We describe a new direct method to estimate bipartite mutual information of a classical spin system based on Monte Carlo sampling enhanced by autoregressive neural networks. It all…
Simulating first-order phase transition with hierarchical autoregressive networks
Piotr Białas, Paulina Czarnota, Piotr Korcyl +1
We apply the Hierarchical Autoregressive Neural (HAN) network sampling algorithm to the two-dimensional -state Potts model and perform simulations around the phase transition at…
Scale setting and the light baryon spectrum in QCD with Wilson fermions
Gunnar S. Bali, Sara Collins, Peter Georg +7
We determine the light baryon spectrum on ensembles generated by the Coordinated Lattice Simulations (CLS) effort, employing flavours of non-perturbatively improved Wilso…
Hierarchical autoregressive neural networks for statistical systems
Piotr Białas, Piotr Korcyl, Tomasz Stebel
It was recently proposed that neural networks could be used to approximate many-dimensional probability distributions that appear e.g. in lattice field theories or statistical mech…
Gradient estimators for normalising flows
Piotr Bialas, Piotr Korcyl, Tomasz Stebel
Recently a machine learning approach to Monte-Carlo simulations called Neural Markov Chain Monte-Carlo (NMCMC) is gaining traction. In its most popular form it uses neural networks…