Fetching the paper…
Reading the bibliography…
We develop a multiscale approach to estimate high-dimensional probability distributions from a dataset of physical fields or configurations observed in experiments or simulations.
R. Bellman and R. Kalaba, A mathematical theory of adaptive control processes, Proceedings of the National Academy of Sciences 45
1959
Earlier work this paper cites.
A. N. Kolmogorov, A refinement of previous hypotheses concerning the local structure of turbulence in a viscous incompressible fluid at high reynolds number, Journal of Fluid Mechanics 13
1962
Earlier work this paper cites.
K. G. Wilson, Renormalization group and critical phenomena. ii. phase-space cell analysis of critical behavior, Physical Review B 4
1971
Earlier work this paper cites.
K. G. Wilson and M. E. Fisher, Critical exponents in 3.99 dimensions, Physical Review Letters 28
1972
Earlier work this paper cites.
M. E. Fisher, The renormalization group in the theory of critical behavior, Reviews of Modern Physics 46
1974
Earlier work this paper cites.
B. B. Mandelbrot, Multifractals and 1/? noise wild self-affinity in physics (1963–1976),
1976
Earlier work this paper cites.
L. P. Kadanoff, A. Houghton, and M. C. Yalabik, Variational approximations for renormalization group transformations, Journal of Statistical Physics 14
1976
Earlier work this paper cites.
G. M. Torrie and J. P. Valleau, Nonphysical sampling distributions in monte carlo free-energy estimation: Umbrella sampling, Journal of Computational Physics 23
1977
Earlier work this paper cites.
P. C. Hohenberg and B. I. Halperin, Theory of dynamic critical phenomena, Reviews of Modern Physics 49
1977
Earlier work this paper cites.
S. Geman and D. Geman, Stochastic relaxation, gibbs distributions, and the bayesian restoration of images, IEEE Transactions on pattern analysis and machine intelligence , 721 (1984)
1984
Earlier work this paper cites.
U. Frisch and G. Parisi, Fully developed turbulence and intermittency, Proceedings of the International Summer School on Turbulence and Predictability in Geophysical Fluid Dynamics and Climate Dynamics , 84?88 (1985)
1985
Earlier work this paper cites.
A. Milchev, D. Heermann, and K. Binder, Finite-size scaling analysis of the ϕ \phi 4 field theory on the square lattice, Journal of statistical physics 44
1986
Earlier work this paper cites.
R. H. Swendsen and J.-S. Wang, Nonuniversal critical dynamics in monte carlo simulations, Physical review letters 58
1987
Earlier work this paper cites.
S. Duane, A. D. Kennedy, B. J. Pendleton, and D. Roweth, Hybrid monte carlo, Physics letters B 195
1987
Earlier work this paper cites.
U. Wolff, Comparison between cluster monte carlo algorithms in the ising model, Physics Letters B 228
1989
Earlier work this paper cites.
J. Goodman and A. D. Sokal, Multigrid monte carlo method. conceptual foundations, Physical Review D 40
1989
Earlier work this paper cites.
U. Frisch, From global scaling, a la kolmogorov, to local multifractal scaling in fully developed turbulence, Proceedings of the Royal Society of London. Series A: Mathematical and Physical Sciences 434
1991
Earlier work this paper cites.
Y. Meyer, Wavelets and Operators (Advanced mathematics. Cambridge university press, 1992)
1992
Earlier work this paper cites.
I. Daubechies, Ten lectures on wavelets (SIAM, 1992)
1992
Earlier work this paper cites.
E. Marinari and G. Parisi, Simulated tempering: a new monte carlo scheme, EPL (Europhysics Letters) 19
1992
Earlier work this paper cites.
1992
Earlier work this paper cites.
J.-F. Muzy, E. Bacry, and A. Arneodo, The multifractal formalism revisited with wavelets, International Journal of Bifurcation and Chaos 4
1994
Earlier work this paper cites.
P. M. Chaikin, T. C. Lubensky, and T. A. Witten, Principles of condensed matter physics , Vol. 10 (Cambridge university press Cambridge, 1995)
1995
Earlier work this paper cites.
J. Cardy, Scaling and renormalization in statistical physics , Vol. 5 (Cambridge university press, 1996)
1996
Earlier work this paper cites.
S. C. Zhu, Y. N. Wu, and D. Mumford, Minimax entropy principle and its application to texture modeling, Neural computation 9
1997
Earlier work this paper cites.
A. Gelman, W. R. Gilks, and G. O. Roberts, Weak convergence and optimal scaling of random walk metropolis algorithms, The annals of applied probability 7
1997
Earlier work this paper cites.
G. Battle, Wavelets and renormalization , Vol. 10 (World Scientific, 1999)
1999
Earlier work this paper cites.
M. Stephane, A wavelet tour of signal processing (1999)
1999
Earlier work this paper cites.
M. Hasenbusch, A monte carlo study of leading order scaling corrections of 4 theory on a three-dimensional lattice, Journal of Physics A: Mathematical and General 32
1999
Cited alongside, same era.
J. Portilla and E. P. Simoncelli, A parametric texture model based on joint statistics of complex wavelet coefficients, International journal of computer vision 40
2000
Cited alongside, same era.
E. Bacry, J. Delour, and J. F. Muzy, Multifractal random walk, Phys. Rev. E 64
2001
Cited alongside, same era.
D. Frenkel and B. Smit, Understanding molecular simulation: from algorithms to applications , Vol. 1 (Elsevier, 2001)
2001
Cited alongside, same era.
M. Bartelmann and P. Schneider, Weak gravitational lensing, Physics Reports 340
2001
Cited alongside, same era.
S. Mallat, Understanding deep convolutional networks, Phil. Trans. of Royal Society A 374
2016
Later among the works it cites.
M. Altaisky, Unifying renormalization group and the continuous wavelet transform, Physical Review D 93
2016
Later among the works it cites.
J. Kaupužs, R. Melnik, and J. Rimšāns, Corrections to finite-size scaling in the φ \varphi 4 model on square lattices, International Journal of Modern Physics C 27
2016
Later among the works it cites.
J. M. Z. Matilla, Z. Haiman, D. Hsu, A. Gupta, and A. Petri, Do dark matter halos explain lensing peaks?, Physical Review D 94
2016
Later among the works it cites.
T. Kacprzak, D. Kirk, O. Friedrich, A. Amara, A. Refregier, L. Marian, J. Dietrich, E. Suchyta, J. Aleksić, D. Bacon, et al. , Cosmology constraints from shear peak statistics in dark energy survey science verification data, Monthly Notices of the Royal Astronomical Society 463
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
J. Zinn-Justin, Quantum field theory and critical phenomena , Vol. 113 (Clarendon Press, Oxford, 2002)
2002
Cited alongside, same era.
D. Ron, R. H. Swendsen, and A. Brandt, Inverse monte carlo renormalization group transformations for critical phenomena, Physical review letters 89
2002
Cited alongside, same era.
J. Berges, N. Tetradis, and C. Wetterich, Non-perturbative renormalization flow in quantum field theory and statistical physics, Physics Reports 363
2002
Cited alongside, same era.
B. Derrida, J. Lebowitz, and E. Speer, Large deviation of the density profile in the steady state of the open symmetric simple exclusion process, Journal of statistical physics 107
2002
Cited alongside, same era.
A. Tröster, C. Dellago, and W. Schranz, Free energies of the ϕ \phi 4 model from wang-landau simulations, Physical Review B 72
2005
Cited alongside, same era.
E. Schneidman, M. J. Berry, R. Segev, and W. Bialek, Weak pairwise correlations imply strongly correlated network states in a neural population, Nature 440
2006
Cited alongside, same era.
W. Krauth, Statistical mechanics: algorithms and computations , Vol. 13 (OUP Oxford, 2006)
2006
Cited alongside, same era.
2016
Later among the works it cites.
J. Carrasquilla and R. G. Melko, Machine learning phases of matter, Nature Physics 13
2017
Later among the works it cites.
H. W. Lin, M. Tegmark, and D. Rolnick, Why does deep and cheap learning work so well?, Journal of Statistical Physics 168
2017
Later among the works it cites.
S. Cocco, C. Feinauer, M. Figliuzzi, R. Monasson, and M. Weigt, Inverse statistical physics of protein sequences: a key issues review, Reports on Progress in Physics 81
2018
Later among the works it cites.
S.-H. Li and L. Wang, Neural network renormalization group, Physical review letters 121
2018
Later among the works it cites.
A. Gupta, J. M. Z. Matilla, D. Hsu, and Z. Haiman, Non-gaussian information from weak lensing data via deep learning, Physical Review D 97
2018
Later among the works it cites.
N. Martinet, P. Schneider, H. Hildebrandt, H. Shan, M. Asgari, J. P. Dietrich, J. Harnois-Déraps, T. Erben, A. Grado, C. Heymans, et al. , Kids-450: cosmological constraints from weak-lensing peak statistics–ii: Inference from shear peaks using n-body simulations, Monthly Notices of the Royal Astronomical Society 474
2018
Later among the works it cites.
H. Shan, X. Liu, H. Hildebrandt, C. Pan, N. Martinet, Z. Fan, P. Schneider, M. Asgari, J. Harnois-Déraps, H. Hoekstra, et al. , Kids-450: cosmological constraints from weak lensing peak statistics–i. inference from analytical prediction of high signal-to-noise ratio convergence peaks, Monthly Notices of the Royal Astronomical Society 474
2018
Later among the works it cites.
S. Iso, S. Shiba, and S. Yokoo, Scale-invariant feature extraction of neural network and renormalization group flow, Physical review E 97
2018
Later among the works it cites.
W. Zhong, G. T. Barkema, D. Panja, and R. C. Ball, Critical dynamical exponent of the two-dimensional scalar ϕ \phi 4 model with local moves, Physical Review E 98
2018
Later among the works it cites.
N. Perraudin, A. Srivastava, A. Lucchi, T. Kacprzak, T. Hofmann, and A. Réfrégier, Cosmological n-body simulations: a challenge for scalable generative models, Computational Astrophysics and Cosmology 6
2019
Later among the works it cites.
F. Noé, S. Olsson, J. Köhler, and H. Wu, Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning, Science 365
2019
Later among the works it cites.
J. Bruna and S. Mallat, Multiscale sparse microcanonical models, Mathematical Statistics and Learning 1
2019
Later among the works it cites.
G. Lee, R. Gommers, F. Waselewski, K. Wohlfahrt, and A. O’Leary, Pywavelets: A python package for wavelet analysis, Journal of Open Source Software 4
2019
Later among the works it cites.
E. Allys, T. Marchand, J.-F. Cardoso, F. Villaescusa-Navarro, S. Ho, and S. Mallat, New interpretable statistics for large-scale structure analysis and generation, Physical Review D 102
2020
Later among the works it cites.
2021
Later among the works it cites.
J. Sethna, Statistical mechanics: entropy, order parameters, and complexity , Vol. 14 (Oxford University Press, USA, 2021)
2021
Later among the works it cites.
K. Shiina, H. Mori, Y. Tomita, H. K. Lee, and Y. Okabe, Inverse renormalization group based on image super-resolution using deep convolutional networks, Scientific Reports 11
2021
Later among the works it cites.
S. Zhang and S. Mallat, Maximum entropy models from phase harmonic covariances, Applied and Computational Harmonic Analysis 53
2021
Later among the works it cites.
S. Cheng and B. Mé nard, Weak lensing scattering transform: dark energy and neutrino mass sensitivity, Monthly Notices of the Royal Astronomical Society 10.1093/mnras/stab2102 (2021)
2021
Later among the works it cites.
L. Wasserman, All of statistics, in All of Statistics (Springer, 2021)
2021
Later among the works it cites.
D. Bachtis, G. Aarts, F. Di Renzo, and B. Lucini, Inverse renormalization group in quantum field theory, Physical Review Letters 128
2022
Closest in time.
D. A. Roberts, S. Yaida, and B. Hanin, The Principles of Deep Learning Theory: An Effective Theory Approach to Understanding Neural Networks (Cambridge University Press, 2022)
2022
Closest in time.