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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Conditional normalizing flow for Markov chain Monte Carlo sampling in the critical region of lattice field theory
Ankur Singha, Dipankar Chakrabarti, Vipul Arora
Physical review. D/Physical review. D. (2023) Vol. 107, Iss. 1
Open Access | Times Cited: 23

Showing 23 citing articles:

Exploring QCD matter in extreme conditions with Machine Learning
Kai Zhou, Lingxiao Wang, Long-Gang Pang, et al.
Progress in Particle and Nuclear Physics (2023) Vol. 135, pp. 104084-104084
Open Access | Times Cited: 37
Aspects of scaling and scalability for flow-based sampling of lattice QCD
Ryan Abbott, Michael S. Albergo, Aleksandar Botev, et al.
The European Physical Journal A (2023) Vol. 59, Iss. 11
Open Access | Times Cited: 23
Advances in machine-learning-based sampling motivated by lattice quantum chromodynamics
K. Cranmer, Gurtej Kanwar, Sébastien Racanière, et al.
Nature Reviews Physics (2023) Vol. 5, Iss. 9, pp. 526-535
Open Access | Times Cited: 19
Detecting and mitigating mode-collapse for flow-based sampling of lattice field theories
Kim A. Nicoli, Christopher J. Anders, Tobias Hartung, et al.
Physical review. D/Physical review. D. (2023) Vol. 108, Iss. 11
Open Access | Times Cited: 16
Diffusion models as stochastic quantization in lattice field theory
Lili Wang, Gert Aarts, Kai Zhou
Journal of High Energy Physics (2024) Vol. 2024, Iss. 5
Open Access | Times Cited: 5
Applications of flow models to the generation of correlated lattice QCD ensembles
Ryan Abbott, Aleksandar Botev, Denis Boyda, et al.
Physical review. D/Physical review. D. (2024) Vol. 109, Iss. 9
Open Access | Times Cited: 5
Normalizing flows as an enhanced sampling method for atomistic supercooled liquids
Gerhard Jung, Giulio Biroli, Ludovic Berthier
Machine Learning Science and Technology (2024) Vol. 5, Iss. 3, pp. 035053-035053
Open Access | Times Cited: 4
Exploring QCD matter in extreme conditions with Machine Learning
Kai Zhou, Lingxiao Wang, Long-Gang Pang, et al.
arXiv (Cornell University) (2023)
Open Access | Times Cited: 10
Conditioning Boltzmann generators for rare event sampling
Sebastian Falkner, Alessandro Coretti, Salvatore Romano, et al.
Machine Learning Science and Technology (2023) Vol. 4, Iss. 3, pp. 035050-035050
Open Access | Times Cited: 9
Comparison of Affine and Rational Quadratic Spline Coupling and Autoregressive Flows through Robust Statistical Tests
A. Coccaro, Marco Letizia, Humberto Reyes-González, et al.
Symmetry (2024) Vol. 16, Iss. 8, pp. 942-942
Open Access | Times Cited: 2
The NFLikelihood: An unsupervised DNNLikelihood from normalizing flows
Humberto Reyes-González, Riccardo Torre
SciPost Physics Core (2024) Vol. 7, Iss. 3
Open Access | Times Cited: 2
Sampling U(1) gauge theory using a retrainable conditional flow-based model
Ankur Singha, Dipankar Chakrabarti, Vipul Arora
Physical review. D/Physical review. D. (2023) Vol. 108, Iss. 7
Open Access | Times Cited: 5
Parallel tempered metadynamics: Overcoming potential barriers without surfing or tunneling
Timo Eichhorn, Gianluca Fuwa, Christian Hoelbling, et al.
Physical review. D/Physical review. D. (2024) Vol. 109, Iss. 11
Open Access | Times Cited: 1
Sampling U(1) gauge theory using a re-trainable conditional flow-based model
Ankur Singha, Dipankar Chakrabarti, Vikas Arora
arXiv (Cornell University) (2023)
Open Access | Times Cited: 3
Diffusion Models as Stochastic Quantization in Lattice Field Theory
Lingxiao Wang, Gert Aarts, Kai Zhou
arXiv (Cornell University) (2023)
Open Access | Times Cited: 2
Study of topological quantities of lattice QCD with a modified Wasserstein generative adversarial network
Lin Gao, He-Ping Ying, J. B. Zhang
Physical review. D/Physical review. D. (2024) Vol. 109, Iss. 7
Open Access
AdvNF: Reducing mode collapse in conditional normalising flows using adversarial learning
Vikas Kanaujia, Mathias S. Scheurer, Vipul Arora
SciPost Physics (2024) Vol. 16, Iss. 5
Open Access
Report on 2401.15948v1
Vikas Kanaujia, Mathias S. Scheurer, Vipul Arora
arXiv (Cornell University) (2024)
Open Access
Report on 2401.15948v1
Vikas Kanaujia, Mathias S. Scheurer, Vipul Arora
arXiv (Cornell University) (2024)
Open Access
Global sampling of Feynman's diagrams through normalizing flow
Luca Leoni, Cesare Franchini
Physical Review Research (2024) Vol. 6, Iss. 3
Open Access
Parallel Tempered Metadynamics: Overcoming potential barriers without surfing or tunneling
Timo Eichhorn, Gianluca Fuwa, Christian Hoelbling, et al.
arXiv (Cornell University) (2023)
Open Access | Times Cited: 1
NeuLat: a toolbox for neural samplers in lattice field theories
Kim A. Nicoli, Christopher J. Anders, Lena Funcke, et al.
Proceedings of The 39th International Symposium on Lattice Field Theory — PoS(LATTICE2022) (2023), pp. 286-286
Open Access | Times Cited: 1
Study of topological quantities of lattice QCD by a modified Wasserstein generative adversarial network
Lin Gao, Zhen Cheng, He-Ping Ying, et al.
arXiv (Cornell University) (2023)
Open Access
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