Fetching the paper…
Reading the bibliography…
General-purpose Markov Chain Monte Carlo sampling algorithms suffer from a dramatic reduction in efficiency as the system being studied is driven towards a critical point.
1901
Earlier work this paper cites.
1902
Earlier work this paper cites.
1904
Earlier work this paper cites.
1906
Earlier work this paper cites.
1906
Earlier work this paper cites.
E. J. Bekkers, B-spline CNNs on Lie groups (2019), arXiv:1909.12057 [cs.LG]
1909
Earlier work this paper cites.
S. Kullbach and R. A. Leibler, On information and sufficiency, Ann. Math. Statist. 22
1951
Earlier work this paper cites.
N. Metropolis, A. W. Rosenbluth, M. N. Rosenbluth, A. H. Teller, and E. Teller, Equation of state calculations by fast computing machines, J. Chem. Phys. 21
1953
Earlier work this paper cites.
L. P. Kadanoff, Scaling laws for Ising models near T c T_{c} , Physics 2
1966
Earlier work this paper cites.
K. L. Chung, Markov Chains with Stationary Transition Probabilities , 2nd ed. (Springer-Verlag, 1967)
1967
Earlier work this paper cites.
W. K. Hastings, Monte Carlo sampling methods using Markov chains and their applications, Biometrika 57
1970
Earlier work this paper cites.
K. G. Wilson and J. Kogut, The renormalization group and the ϵ \epsilon expansion, Phys. Rep. 12
1973
Earlier work this paper cites.
B. Efron, Bootstrap methods: another look at the jackknife, Ann. Statist. 7
1979
Earlier work this paper cites.
J. A. Gregory and R. Delbourgo, C2 rational quadratic spline interpolation to monotonic data, IMA Journal of Numerical Analysis 3
1983
Earlier work this paper cites.
B. Efron and R. Tibshirani, Bootstrap methods for standard errors, confidence intervals, and other measures of statistical accuracy, Statistical Science 1
1986
Earlier work this paper cites.
D. E. Rumelhart, G. E. Hinton, and R. J. Williams, Learning representations by back-propagating errors, Nature 323
1986
Earlier work this paper cites.
S. Duane, A. D. Kennedy, B. J. Pendleton, and D. Roweth, Hybrid Monte Carlo, Phys. Lett. B 195
1987
Earlier work this paper cites.
R. H. Swendsen and J. S. Wang, Nonuniversal critical dynamics in Monte Carlo simulations, Phys. Rev. Lett. 58
1987
Earlier work this paper cites.
N. Madras and A. D. Sokal, J. Statist. Phys 50
1988
Earlier work this paper cites.
U. Wolff, Collective Monte Carlo updating for spin systems, Phys. Rev. Lett. 62
1989
Earlier work this paper cites.
U. Wolff, Critical slowing down, Nucl. Phys. B (Proc. Suppl.) 18
1990
Earlier work this paper cites.
A. D. Kennedy and B. J. Pendleton, Acceptances and autocorrelations in hybrid Monte Carlo, Nucl. Phys. B 20
1991
Earlier work this paper cites.
K. Hornik, Approximation capabilitities of multilayer feedforward networks, Neural Networks 4
1991
Earlier work this paper cites.
M. Campostrini, P. Rossi, and E. Vicari, Monte Carlo simulation of CP N − 1 \mathrm{CP}^{N-1} models, Phys. Rev. D 46
1992
Earlier work this paper cites.
E. Vicari, Monte carlo simulation of lattice ℂ ℙ N − 1 \mathbb{CP}^{N-1} models at large N N , Phys. Lett. B 309
1993
Earlier work this paper cites.
D. Kusnezov and J. Sloan, Global demons in field theory. critical slowing down in the XY model, Nucl. Phys. B 409
1993
Earlier work this paper cites.
H. G. Evertz, G. Lana, and M. Marcu, Cluster algorithm for vertex models, Phys. Rev. Lett. 70
1993
Earlier work this paper cites.
L. Tierney, Markov chains for exploring posterior distributions, Ann. Statist. 22
1994
Earlier work this paper cites.
B. Allés, G. Boyd, M. D’Elia, A. Di Giacomo, and E. Vicari, Hybrid Monte Carlo and topological modes of full QCD, Phys. Lett. B 389
1996
Earlier work this paper cites.
A. D. Sokal, Monte Carlo methods in statistical mechanics: Foundations and new algorithms, in Functional Integration (1997) pp. 131–192
1997
Earlier work this paper cites.
N. Prokof’ev, I. S. Tupitsyn, and B. V. Svistunov, Exact, complete, and universal continuous-time worldline Monte Carlo approach to the statistics of discrete quantum systems, J. Exp. Theor. Phys. 87
1998
Earlier work this paper cites.
S. Caracciolo and A. Pelissetto, Corrections to finite-size scaling in the lattice N N -vector model for N = ∞ N=\infty , Phys. Rev. D 58
1998
Earlier work this paper cites.
L. Del Debbio, H. Panagopoulos, P. Rossi, and E. Vicari, Spectrum of confining strings in SU(N) gauge theories, J. High Energy Phys. 2002
2002
Earlier work this paper cites.
2002
Earlier work this paper cites.
2003
Earlier work this paper cites.
C. Meng, Y. Song, J. Song, and S. Ermon, Gaussianization flows (2020), arXiv:2003.01941 [cs.LG]
2003
Cited alongside, same era.
B. Kaufmann, Fitting a sum of exponentials to numerical data (2003), arXiv:0305019 [physics.data-an]
2003
Cited alongside, same era.
U. Wolff, Monte Carlo errors with less errors (2003), arXiv:0306017 [hep-lat]
2003
Cited alongside, same era.
L. Del Debbio, G. M. Manca, and E. Vicari, Critical slowing down of topological modes, Phys. Lett. B 594
2004
Cited alongside, same era.
2005
Cited alongside, same era.
L. Huang and L. Wang, Accelerated Monte Carlo simulations with restricted Boltzmann machines, Phys. Rev. B 95
2017
Later among the works it cites.
2017
Later among the works it cites.
2017
Later among the works it cites.
2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
T. Minka, Divergence measures and message passing , Tech. Rep. (2005)
2005
Cited alongside, same era.
2005
Cited alongside, same era.
2006
Cited alongside, same era.
2006
Cited alongside, same era.
2007
Cited alongside, same era.
Y. Bengio and Y. LeCun, Scaling learning algorithms towards AI, in Large-scale Kernel Machines (MIT Press, 2007)
2007
Cited alongside, same era.
2008
Cited alongside, same era.
2017
Later among the works it cites.
I. Loshchilov and F. Hutter, Decoupled weight decay regularization (2017), arXiv:1711.05101 [cs.LG]
2017
Later among the works it cites.
C. Zhang, Q. Liao, A. Rakhlin, B. Miranda, N. Golowich, and T. Poggio, Theory of deep learning III : Generalization properties of SGD, in CBMM Memo No. 067 (2017)
2017
Later among the works it cites.
H. Mhaskar, Q. Liao, and T. Poggio, When and why are deep networks bettter than shallow ones, in When and Why Are Deep Networks Better than Shallow Ones? (2017)
2017
Later among the works it cites.
2017
Later among the works it cites.
2018
Later among the works it cites.
2018
Later among the works it cites.
2018
Later among the works it cites.
2018
Later among the works it cites.
2018
Later among the works it cites.
2018
Later among the works it cites.
2018
Later among the works it cites.
2018
Later among the works it cites.
2018
Later among the works it cites.
D.-X. Zhou, Universality of deep convolutional neural networks (2018), arXiv:1805.10769 [cs.LG]
2018
Later among the works it cites.
2018
Later among the works it cites.
T. S. Cohen, M. Geiger, J. Koehler, and M. Welling, Spherical CNNs (2018), arXiv:1801.10130 [cs.LG]
2018
Later among the works it cites.
2019
Later among the works it cites.
2019
Later among the works it cites.
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala, PyTorch: An imperative style, high-performance deep learning library, in Advances in Neural Information Processing Systems 32 , edited by H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, and R. Garnett (Curran Associates, Inc., 2019) pp. 8024–8035
2019
Later among the works it cites.
Z. Kassabov, Reportengine: A framework for declarative data analysis (2019)
2019
Later among the works it cites.
A. Heinecke, J. Ho, and W.-L. Hwang, Refinement and universal approximation via sparsely connected relu convolution nets, IEEE Signal Processing Letters 27
2020
Later among the works it cites.
2021
Closest in time.
2021
Closest in time.
S. Foreman, X.-Y. Jin, and J. C. Osborn, Deep learning Hamiltonian Monte Carlo (2021), arXiv:2105-03418 [hep-lat]
2021
Closest in time.
2021
Closest in time.
2021
Closest in time.
M. R. Wilson, J. Marsh Rossney, and L. Del Debbio, ANVIL (version 0.9) (2021), https://doi.org/10.5281/zenodo.4792249
2021
Closest in time.
2021
Closest in time.
2021
Closest in time.