Projects per year
Abstract
We train a set of Restricted Boltzmann Machines (RBMs) on one- and two-dimensional Ising spin configurations at various values of temperature, generated using Monte Carlo simulations. We validate the training procedure by monitoring several estimators, including measurements of
the log-likelihood, with the corresponding partition functions estimated using annealed importance
sampling. The effects of various choices of hyper-parameters on training the RBM are discussed
in detail, with a generic prescription provided. Finally, we present a closed form expression for
extracting the values of couplings, for every n-point interaction between the visible nodes of an
RBM, in a binary system such as the Ising model. We aim at using this study as the foundation for
further investigations of less well-known systems.
the log-likelihood, with the corresponding partition functions estimated using annealed importance
sampling. The effects of various choices of hyper-parameters on training the RBM are discussed
in detail, with a generic prescription provided. Finally, we present a closed form expression for
extracting the values of couplings, for every n-point interaction between the visible nodes of an
RBM, in a binary system such as the Ising model. We aim at using this study as the foundation for
further investigations of less well-known systems.
Original language | English |
---|---|
Number of pages | 31 |
Journal | Physical review B |
DOIs | |
Publication status | Published - 7 Aug 2019 |
Keywords
- physics.comp-ph
- cond-mat.stat-mech
- hep-lat
Fingerprint
Dive into the research topics of 'Machine learning determination of dynamical parameters: The Ising model case'. Together they form a unique fingerprint.Projects
- 2 Finished
-
Particle Theory at the Higgs Centre
Ball, R., Boyle, P., Del Debbio, L., Gardi, E., Horsley, R., Kennedy, A., O'Connell, D., Smillie, J. & Zwicky, R.
1/10/17 → 30/09/21
Project: Research
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High-performance computing at the high-energy frontier: results for the LHC
1/01/15 → 31/12/19
Project: Research
Profiles
-
Ava Khamseh
- MRC Human Genetics Unit
- School of Informatics - Lecturer in Biomedical Artificial Intelligence
- Institute for Adaptive and Neural Computation
Person: Academic: Research Active , Academic: Research Active (Research Assistant)