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Recent applications of machine-learned normalizing flows to sampling in lattice field theory suggest that such methods may be able to mitigate critical slowing down and topological freezing.
C. Lehner, et al., Opportunities for Lattice QCD in Quark and Lepton Flavor Physics · 1904
Earlier work this paper cites.
A.S. Kronfeld, D.G. Richards, W. Detmold, R. Gupta, H.W. Lin, K.F. Liu, A.S. Meyer, R. Sufian, S. Syritsyn, Lattice QCD and Neutrino-Nucleus Scattering · 1904
Earlier work this paper cites.
V. Cirigliano, Z. Davoudi, T. Bhattacharya, T. Izubuchi, P.E. Shanahan, S. Syritsyn, M.L. Wagman, The Role of Lattice QCD in Searches for Violations of Fundamental Symmetries and Signals for New Physics · 1904
Earlier work this paper cites.
W. Detmold, R.G. Edwards, J.J. Dudek, M. Engelhardt, H.W. Lin, S. Meinel, K. Orginos, P. Shanahan, Hadrons and Nuclei · 1904
Earlier work this paper cites.
A. Bazavov, F. Karsch, S. Mukherjee, P. Petreczky, Hot-dense Lattice QCD: USQCD whitepaper 2018 · 1904
Earlier work this paper cites.
B. Joó, C. Jung, N.H. Christ, W. Detmold, R. Edwards, M. Savage, P. Shanahan, Status and Future Perspectives for Lattice Gauge Theory Calculations to the Exascale and Beyond · 1904
Earlier work this paper cites.
R.C. Brower, A. Hasenfratz, E.T. Neil, S. Catterall, G. Fleming, J. Giedt, E. Rinaldi, D. Schaich, E. Weinberg, O. Witzel, Lattice Gauge Theory for Physics Beyond the Standard Model · 1904
Earlier work this paper cites.
M.S. Albergo, G. Kanwar, P.E. Shanahan, Flow-based generative models for Markov chain Monte Carlo in lattice field theory · 1904
Earlier work this paper cites.
K.A. Nicoli, S. Nakajima, N. Strodthoff, W. Samek, K.R. Müller, P. Kessel, Asymptotically unbiased estimation of physical observables with neural samplers · 1910
Earlier work this paper cites.
G. Papamakarios, E. Nalisnick, D.J. Rezende, S. Mohamed, B. Lakshminarayanan, Normalizing flows for probabilistic modeling and inference (2019) · 1912
Earlier work this paper cites.
The Annals of Mathematical Statistics 22
S. Kullback, R.A. Leibler, On Information and Sufficiency · 1951
Earlier work this paper cites.
J. Chem. Phys. 21
N. Metropolis, A.W. Rosenbluth, M.N. Rosenbluth, A.H. Teller, E. Teller, Equation of state calculations by fast computing machines · 1953
Earlier work this paper cites.
Biometrika 57
W.K. Hastings, Monte Carlo Sampling Methods Using Markov Chains and Their Applications · 1970
Earlier work this paper cites.
Phys. Lett. B 99
D.H. Weingarten, D.N. Petcher, Monte Carlo Integration for Lattice Gauge Theories with Fermions · 1981
Earlier work this paper cites.
Nucl. Phys. B 180
F. Fucito, E. Marinari, G. Parisi, C. Rebbi, A Proposal for Monte Carlo Simulations of Fermionic Systems · 1981
Earlier work this paper cites.
Phys. Lett. B 195
S. Duane, A.D. Kennedy, B.J. Pendleton, D. Roweth, Hybrid Monte Carlo · 1987
Earlier work this paper cites.
Nucl. Phys. Proc. Suppl. 17
U. Wolff, Critical slowing down · 1990
Earlier work this paper cites.
R.M. Neal, Probabilistic inference using Markov chain Monte Carlo methods (Department of Computer Science, University of Toronto Toronto, ON, Canada, 1993), chap. 5
1993
Earlier work this paper cites.
the Annals of Statistics pp. 1701–1728 (1994)
L. Tierney, Markov chains for exploring posterior distributions · 1994
Earlier work this paper cites.
Lecture Notes in Statistics 118
R.M. Neal, Bayesian Learning for Neural Networks · 1996
Earlier work this paper cites.
10.5170/CERN-2000-002
F. Jegerlehner, R.D. Kenway, G. Martinelli, C. Michael, O. Pene, B. Petersson, R. Petronzio, C.T. Sachrajda, K. Schilling, Requirements for high performance computing for lattice QCD: Report of the ECFA working panel (2000) · 2000
Earlier work this paper cites.
A. Doucet, N. De Freitas, N.J. Gordon, et al., Sequential Monte Carlo methods in practice , vol. 1 (Springer, 2001)
2001
Earlier work this paper cites.
J.S. Liu, J.S. Liu, Monte Carlo strategies in scientific computing , vol. 10 (Springer, 2001)
2001
Earlier work this paper cites.
Phys. Lett. B 519
M. Hasenbusch, Speeding up the hybrid Monte Carlo algorithm for dynamical fermions · 2001
Earlier work this paper cites.
D.J. Rezende, G. Papamakarios, S. Racanière, M.S. Albergo, G. Kanwar, P.E. Shanahan, K. Cranmer, Normalizing Flows on Tori and Spheres (2020) · 2002
Earlier work this paper cites.
Advances in Neural Information Processing Systems 33
H. Wu, J. Köhler, F. Noé, Stochastic normalizing flows · 2002
Earlier work this paper cites.
Nucl. Phys. B Proc. Suppl. 106
A. Ukawa, Computational cost of full QCD simulations experienced by CP-PACS and JLQCD Collaborations · 2002
Earlier work this paper cites.
Nucl. Phys. B Proc. Suppl. 106
T. Lippert, Cost of QCD simulations with n(f) = 2 dynamical Wilson fermions · 2002
Earlier work this paper cites.
G. Kanwar, M.S. Albergo, D. Boyda, K. Cranmer, D.C. Hackett, S. Racanière, D.J. Rezende, P.E. Shanahan, Equivariant flow-based sampling for lattice gauge theory · 2003
Earlier work this paper cites.
D. Bachtis, G. Aarts, B. Lucini, Extending machine learning classification capabilities with histogram reweighting · 2004
Earlier work this paper cites.
O. Johnson, Information theory and the central limit theorem (World Scientific, 2004)
2004
Earlier work this paper cites.
Nucl. Phys. B Proc. Suppl. 129
M. Hasenbusch, Full QCD algorithms towards the chiral limit · 2004
Earlier work this paper cites.
Phys. Lett. B 594
L. Del Debbio, G.M. Manca, E. Vicari, Critical slowing down of topological modes · 2004
Earlier work this paper cites.
Journal of Machine Learning Research 6
A. Hyvärinen, P. Dayan, Estimation of non-normalized statistical models by score matching · 2005
Earlier work this paper cites.
T. DeGrand, C.E. Detar, Lattice methods for quantum chromodynamics (2006)
2006
Earlier work this paper cites.
arXiv:hep-lat/0702020
C. Morningstar, The Monte Carlo method in quantum field theory (2007) · 2007
Earlier work this paper cites.
K.A. Nicoli, C.J. Anders, L. Funcke, T. Hartung, K. Jansen, P. Kessel, S. Nakajima, P. Stornati, On Estimation of Thermodynamic Observables in Lattice Field Theories with Deep Generative Models (2020) · 2007
Earlier work this paper cites.
Computing in Science & Engineering 9
J.D. Hunter, Matplotlib: A 2d graphics environment · 2007
Earlier work this paper cites.
D. Boyda, G. Kanwar, S. Racanière, D.J. Rezende, M.S. Albergo, K. Cranmer, D.C. Hackett, P.E. Shanahan, Sampling using S U ( N ) SU(N) gauge equivariant flows · 2008
Earlier work this paper cites.
J. Brannick, R.C. Brower, M.A. Clark, J.C. Osborn, C. Rebbi, Adaptive Multigrid Algorithm for Lattice QCD · 2008
Cited alongside, same era.
S. Schaefer, R. Sommer, F. Virotta, Investigating the critical slowing down of QCD simulations · 2009
Cited alongside, same era.
S. Schaefer, R. Sommer, F. Virotta, Critical slowing down and error analysis in lattice QCD simulations · 2010
Cited alongside, same era.
Y. Nagai, A. Tanaka, A. Tomiya, Self-learning Monte-Carlo for non-abelian gauge theory with dynamical fermions (2020) · 2010
Cited alongside, same era.
10.1007/978-3-642-01850-3
C. Gattringer, C.B. Lang, Quantum chromodynamics on the lattice , vol. 788 (Springer, Berlin, 2010) · 2010
G. Cossu, L. Del Debbio, T. Giani, A. Khamseh, M. Wilson, Machine learning determination of dynamical parameters: The Ising model case · 2019
Later among the works it cites.
Phys. Rev. Lett. 122
D. Wu, L. Wang, P. Zhang, Solving Statistical Mechanics Using Variational Autoregressive Networks · 2019
Later among the works it cites.
Advances in neural information processing systems 32
C. Durkan, A. Bekasov, I. Murray, G. Papamakarios, Neural spline flows · 2019
Later among the works it cites.
URL http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf
A. Paszke, et al., in Advances in Neural Information Processing Systems 32 , ed. by H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, R. Garnett (Curran Associates, Inc., 2019), pp. 8024–8035 · 2019
Later among the works it cites.
J.M. Pawlowski, J.M. Urban, Reducing Autocorrelation Times in Lattice Simulations with Generative Adversarial Networks · 2020
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
R. Babich, J. Brannick, R.C. Brower, M.A. Clark, T.A. Manteuffel, S.F. McCormick, J.C. Osborn, C. Rebbi, Adaptive multigrid algorithm for the lattice Wilson-Dirac operator · 2010
Cited alongside, same era.
X. Glorot, Y. Bengio, Understanding the difficulty of training deep feedforward neural networks pp. 249–256 (2010)
2010
Cited alongside, same era.
Handbook of Markov chain Monte Carlo 2
R.M. Neal, et al., MCMC using Hamiltonian dynamics · 2011
Cited alongside, same era.
C.W. Huang, R.T. Chen, C. Tsirigotis, A. Courville, Convex potential flows: Universal probability distributions with optimal transport and convex optimization (2020) · 2012
Cited alongside, same era.
M. Dibak, L. Klein, F. Noé, Temperature-steerable flows (2020) · 2012
Cited alongside, same era.
S. Schaefer, Status and challenges of simulations with dynamical fermions · 2012
Cited alongside, same era.
M.D. Zeiler, Adadelta: an adaptive learning rate method (2012) · 2012
Cited alongside, same era.
Later among the works it cites.
Advances in Neural Information Processing Systems 33
D. Nielsen, P. Jaini, E. Hoogeboom, O. Winther, M. Welling, Survae flows: Surjections to bridge the gap between vaes and flows · 2020
Later among the works it cites.
Advances in neural information processing systems 33
T. Brown, B. Mann, N. Ryder, M. Subbiah, J.D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, et al., Language models are few-shot learners · 2020
Later among the works it cites.
URL http://github.com/deepmind/dm-haiku
T. Hennigan, T. Cai, T. Norman, I. Babuschkin, Haiku: Sonnet for JAX (2020) · 2020
Later among the works it cites.
Nature 585
C.R. Harris, K.J. Millman, S.J. Van Der Walt, R. Gommers, P. Virtanen, D. Cournapeau, E. Wieser, J. Taylor, S. Berg, N.J. Smith, et al., Array programming with numpy · 2020
Later among the works it cites.
Nature methods 17
P. Virtanen, R. Gommers, T.E. Oliphant, M. Haberland, T. Reddy, D. Cournapeau, E. Burovski, P. Peterson, W. Weckesser, J. Bright, et al., Scipy 1.0: fundamental algorithms for scientific computing in python · 2020
Later among the works it cites.
M.S. Albergo, D. Boyda, D.C. Hackett, G. Kanwar, K. Cranmer, S. Racanière, D.J. Rezende, P.E. Shanahan, Introduction to Normalizing Flows for Lattice Field Theory (2021) · 2021
Later among the works it cites.
D.C. Hackett, C.C. Hsieh, M.S. Albergo, D. Boyda, J.W. Chen, K.F. Chen, K. Cranmer, G. Kanwar, P.E. Shanahan, Flow-based sampling for multimodal distributions in lattice field theory (2021) · 2021
Later among the works it cites.
L. Del Debbio, J.M. Rossney, M. Wilson, Efficient Modelling of Trivializing Maps for Lattice ϕ 4 \phi^{4} Theory Using Normalizing Flows: A First Look at Scalability (2021) · 2021
Later among the works it cites.
S. Foreman, X.Y. Jin, J.C. Osborn, Deep Learning Hamiltonian Monte Carlo (2021) · 2021
Later among the works it cites.
S. Foreman, T. Izubuchi, L. Jin, X.Y. Jin, J.C. Osborn, A. Tomiya, HMC with Normalizing Flows (2021) · 2021
Later among the works it cites.
M.S. Albergo, G. Kanwar, S. Racanière, D.J. Rezende, J.M. Urban, D. Boyda, K. Cranmer, D.C. Hackett, P.E. Shanahan, Flow-based sampling for fermionic lattice field theories (2021) · 2021
Later among the works it cites.
arXiv:2105.12603 [physics.data-an]
M. Gabrié, G.M. Rotskoff, E. Vanden-Eijnden, Adaptive Monte Carlo augmented with normalizing flows (2021) · 2021
Later among the works it cites.
P. de Haan, C. Rainone, M.C.N. Cheng, R. Bondesan, Scaling Up Machine Learning For Quantum Field Theory with Equivariant Continuous Flows (2021) · 2021
Later among the works it cites.
S. Lawrence, Y. Yamauchi, Normalizing Flows and the Real-Time Sign Problem · 2021
Later among the works it cites.
A. Tomiya, Y. Nagai, Gauge covariant neural network for 4 dimensional non-abelian gauge theory (2021) · 2021
Later among the works it cites.
Phys. Rev. Lett. (to appear) (2021)
D. Bachtis, G. Aarts, F. Di Renzo, B. Lucini, Inverse renormalization group in quantum field theory · 2021
Later among the works it cites.
arXiv:2105.05650 [cond-mat.stat-mech]
D. Wu, R. Rossi, G. Carleo, Unbiased Monte Carlo Cluster Updates with Autoregressive Neural Networks (2021) · 2021
Later among the works it cites.
International Conference on Machine Learning pp. 318–330 (2021)
M. Arbel, A. Matthews, A. Doucet, Annealed flow transport Monte Carlo · 2021
Later among the works it cites.
S. Foreman, X.Y. Jin, J.C. Osborn, LeapfrogLayers: A Trainable Framework for Effective Topological Sampling · 2022
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J. Finkenrath, Tackling critical slowing down using global correction steps with equivariant flows: the case of the Schwinger model (2022) · 2022
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R. Abbott, M.S. Albergo, D. Boyda, K. Cranmer, D.C. Hackett, G. Kanwar, S. Racanière, D.J. Rezende, F. Romero-López, P.E. Shanahan, B. Tian, J.M. Urban, Gauge-equivariant flow models for sampling in lattice field theories with pseudofermions · 2022
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X.Y. Jin, Neural Network Field Transformation and Its Application in HMC (2022) · 2022
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J.M. Pawlowski, J.M. Urban, Flow-based density of states for complex actions (2022) · 2022
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M. Gerdes, P. de Haan, C. Rainone, R. Bondesan, M.C.N. Cheng, Learning Lattice Quantum Field Theories with Equivariant Continuous Flows (2022) · 2022
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A. Singha, D. Chakrabarti, V. Arora, Conditional Normalizing flow for Monte Carlo sampling in lattice scalar field theory (2022) · 2022
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URL https://proceedings.mlr.press/v162/matthews22a.html
A. Matthews, M. Arbel, D.J. Rezende, A. Doucet, Continual repeated annealed flow transport Monte Carlo 162 · 2022
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B. Máté, F. Fleuret, Deformation Theory of Boltzmann Distributions (2022) · 2022
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S. Smith, M. Patwary, B. Norick, P. LeGresley, S. Rajbhandari, J. Casper, Z. Liu, S. Prabhumoye, G. Zerveas, V. Korthikanti, et al., Using deepspeed and megatron to train megatron-turing nlg 530b, a large-scale generative language model (2022) · 2022
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