Fetching the paper…
Reading the bibliography…
In this paper, we present the first study that compares different models of Bayesian Neural Networks (BNNs) to predict the posterior distribution of the cosmological parameters directly from the Cosmic Microwave Background temperature and polarization maps.
1901
Earlier work this paper cites.
1901
Earlier work this paper cites.
1902
Earlier work this paper cites.
1902
Earlier work this paper cites.
1902
Earlier work this paper cites.
1904
Earlier work this paper cites.
1904
Earlier work this paper cites.
1905
Earlier work this paper cites.
1906
Earlier work this paper cites.
1906
Earlier work this paper cites.
1907
Earlier work this paper cites.
1907
Earlier work this paper cites.
1910
Earlier work this paper cites.
1911
Earlier work this paper cites.
H. Hotelling, The generalization of student’s ratio, Ann. Math. Statist. 2
1931
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, The Journal of Chemical Physics 21
1953
Earlier work this paper cites.
J. Sanchez, Mardia, k. v., j. t. kent, j. m. bibby: Multivariate analysis, Biometrical Journal 24
1982
Earlier work this paper cites.
G. O. Roberts, A. Gelman, and W. R. Gilks, Weak convergence and optimal scaling of random walk metropolis algorithms, Ann. Appl. Probab. 7
1997
Earlier work this paper cites.
M. Zaldarriaga, Cosmic microwave background polarization experiments, The Astrophysical Journal 503
1998
Earlier work this paper cites.
J. C. Platt, Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods, in ADVANCES IN LARGE MARGIN CLASSIFIERS (MIT Press, 1999) pp. 61–74
1999
Earlier work this paper cites.
G. Efstathiou and J. R. Bond, Cosmic confusion: degeneracies among cosmological parameters derived from measurements of microwave background anisotropies, Monthly Notices of the Royal Astronomical Society 304
1999
Earlier work this paper cites.
B. Zadrozny and C. Elkan, Obtaining calibrated probability estimates from decision trees and naive bayesian classifiers, in Proceedings of the Eighteenth International Conference on Machine Learning , ICML ’01 (Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 2001) pp. 609–616
2001
Earlier work this paper cites.
2002
Earlier work this paper cites.
B. Zadrozny and C. Elkan, Transforming classifier scores into accurate multiclass probability estimates, in Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , KDD ’02 (ACM, New York, NY, USA, 2002) pp. 694–699
2002
Earlier work this paper cites.
S. Dodelson and A. P. L. . 1941-1969)., Modern Cosmology (Elsevier Science, 2003)
2003
Earlier work this paper cites.
M. Zaldarriaga, The polarization of the cosmic microwave background (2003), arXiv:astro-ph/0305272 [astro-ph]
2003
Earlier work this paper cites.
J. Kaplan, J. Delabrouille, P. Fosalba, and C. Rosset, Cmb polarization as complementary information to anisotropies, Comptes Rendus Physique 4
2003
Earlier work this paper cites.
2005
Earlier work this paper cites.
J. Dunkley, M. Bucher, P. G. Ferreira, K. Moodley, and C. Skordis, Fast and reliable Markov chain Monte Carlo technique for cosmological parameter estimation, Monthly Notices of the Royal Astronomical Society 356
2005
Earlier work this paper cites.
P. Naselsky, D. Novikov, and I. Novikov, The Physics of the Cosmic Microwave Background , Cambridge Astrophysics (Cambridge University Press, 2006)
2006
Earlier work this paper cites.
W. A. Fendt and B. D. Wandelt, Pico: Parameters for the Impatient Cosmologist, Astrophys. J. 654
2006
Earlier work this paper cites.
M. Giovannini, Why CMB physics?, Int. J. Mod. Phys. A22
2007
Earlier work this paper cites.
2007
Earlier work this paper cites.
M. Giovannini, A Primer on the Physics of the Cosmic Microwave Background (World Scientific, 2008)
2008
Earlier work this paper cites.
R. Trotta, Bayes in the sky: Bayesian inference and model selection in cosmology, Contemporary Physics 49
2008
Earlier work this paper cites.
A. D. Kiureghian and O. Ditlevsen, Aleatory or epistemic? does it matter?, Structural Safety 31
2009
Earlier work this paper cites.
H. U. Nørgaard-Nielsen, Foreground removal from wmap 5yr temperature maps using an mlp neural network (2010)
2010
Earlier work this paper cites.
D. Blas, J. Lesgourgues, and T. Tram, The cosmic linear anisotropy solving system (CLASS). part II: Approximation schemes, Journal of Cosmology and Astroparticle Physics 2011
2011
Cited alongside, same era.
A. Graves, Practical variational inference for neural networks, in Advances in Neural Information Processing Systems 24 , edited by J. Shawe-Taylor, R. S. Zemel, P. L. Bartlett, F. Pereira, and K. Q. Weinberger (Curran Associates, Inc., 2011) pp. 2348–2356
2011
Cited alongside, same era.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, Imagenet classification with deep convolutional neural networks, in Proceedings of the 25th International Conference on Neural Information Processing Systems - Volume 1 , NIPS’12 (Curran Associates Inc., USA, 2012) pp. 1097–1105
2012
Cited alongside, same era.
C. Howlett, A. Lewis, A. Hall, and A. Challinor, CMB power spectrum parameter degeneracies in the era of precision cosmology, Journal of Cosmology and Astroparticle Physics 2012
2012
S. Ioffe, Batch renormalization: Towards reducing minibatch dependence in batch-normalized models, in NIPS (2017)
2017
Later among the works it cites.
M. Kull, T. S. Filho, and P. Flach, Beta calibration: a well-founded and easily implemented improvement on logistic calibration for binary classifiers, in Proceedings of the 20th International Conference on Artificial Intelligence and Statistics , Proceedings of Machine Learning Research, Vol. 54, edited by A. Singh and J. Zhu (PMLR, Fort Lauderdale, FL, USA, 2017) pp. 623–631
2017
Later among the works it cites.
2017
Later among the works it cites.
P. Collaboration (Planck), Planck 2018 results. VI. Cosmological parameters, arXiv1807.06209v1 (2018a)
2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
2012
Cited alongside, same era.
M. Shiraishi, Probing the early universe with the cmb scalar, vector and tensor bispectrum, Springer Theses 10.1007/978-4-431-54180-6 (2013)
2013
Cited alongside, same era.
A. Lewis, Efficient sampling of fast and slow cosmological parameters, Phys. Rev. D 87
2013
Cited alongside, same era.
L. Wan, M. Zeiler, S. Zhang, Y. L. Cun, and R. Fergus, Regularization of neural networks using dropconnect, in Proceedings of the 30th International Conference on Machine Learning , Proceedings of Machine Learning Research, Vol. 28, edited by S. Dasgupta and D. McAllester (PMLR, Atlanta, Georgia, USA, 2013) pp. 1058–1066
2013
Cited alongside, same era.
2013
Cited alongside, same era.
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, Dropout: A simple way to prevent neural networks from overfitting, Journal of Machine Learning Research 15
2014
Cited alongside, same era.
2014
Cited alongside, same era.
Y. Gal and Z. Ghahramani, Dropout as a Bayesian approximation: Insights and applications, in Deep Learning Workshop, ICML (2015)
2015
Cited alongside, same era.
2018
Later among the works it cites.
S. Mishra-Sharma, D. Alonso, and J. Dunkley, Neutrino masses and beyond- Λ CDM \mathrm{\Lambda}\mathrm{CDM} cosmology with lsst and future cmb experiments, Phys. Rev. D 97
2018
Later among the works it cites.
W. Deng, Dark energy constraints in light of pantheon sne ia, bao, cosmic chronometers and cmb polarization and lensing data, Phys. Rev. D 97
2018
Later among the works it cites.
C. Pitrou, A. Coc, J.-P. Uzan, and E. Vangioni, Precision big bang nucleosynthesis with improved helium-4 predictions, Physics Reports 754
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.
S. He, S. Ravanbakhsh, and S. Ho, Analysis of cosmic microwave background with deep learning (2018)
2018
Later among the works it cites.
2018
Later among the works it cites.
Y. Wen, P. Vicol, J. Ba, D. Tran, and R. Grosse, Flipout: Efficient pseudo-independent weight perturbations on mini-batches, in International Conference on Learning Representations (2018)
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.
Y. Kwon, J.-H. Won, B. Joon Kim, and M. Paik, Ininternational conference on medical imaging with deep learning , 13 (2018)
2018
Later among the works it cites.
2018
Later among the works it cites.
2018
Later among the works it cites.
G. Dorta, S. Vicente, L. Agapito, N. D. F. Campbell, and I. Simpson, Structured uncertainty prediction networks, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition 10.1109/cvpr.2018.00574 (2018)
2018
Later among the works it cites.
2018
Later among the works it cites.
H. U. Nørgaard-Nielsen, Confirmation of the detection of b modes in the planck polarization maps, Astronomische Nachrichten 339
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.
2019
Closest in time.
J. Caldeira, W. Wu, B. Nord, C. Avestruz, S. Trivedi, and K. Story, Deepcmb: Lensing reconstruction of the cosmic microwave background with deep neural networks, Astronomy and Computing 28
2019
Closest in time.
S. He, Y. Li, Y. Feng, S. Ho, S. Ravanbakhsh, W. Chen, and B. Póczos, Learning to predict the cosmological structure formation, Proceedings of the National Academy of Sciences 116
2019
Closest in time.
A. Zonca, L. Singer, D. Lenz, M. Reinecke, C. Rosset, E. Hivon, and K. Gorski, healpy: equal area pixelization and spherical harmonics transforms for data on the sphere in python, Journal of Open Source Software 4
2019
Closest in time.
G. Wang, W. Li, M. Aertsen, J. Deprest, S. Ourselin, and T. Vercauteren, Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks, Neurocomputing 338
2019
Closest in time.
J. Fluri, T. Kacprzak, A. Lucchi, A. Refregier, A. Amara, T. Hofmann, and A. Schneider, Cosmological constraints with deep learning from kids-450 weak lensing maps, Phys. Rev. D 100
2019
Closest in time.
A. D. Cobb, M. D. Himes, F. Soboczenski, S. Zorzan, M. D. O’Beirne, A. G. Baydin, Y. Gal, S. D. Domagal-Goldman, G. N. Arney, and D. A. and, An ensemble of bayesian neural networks for exoplanetary atmospheric retrieval, The Astronomical Journal 158
2019
Closest in time.
Y. Kwon, J.-H. Won, B. J. Kim, and M. C. Paik, Uncertainty quantification using bayesian neural networks in classification: Application to biomedical image segmentation, Computational Statistics and Data Analysis 142
2020
Closest in time.
H. J. Hortua, L. Malago, and R. Volpi, Reliable uncertainties for bayesian neural networks using alpha-divergences, in Uncertainty and Robustness in Deep Learning Workshop, ICML (2020)
2020
Closest in time.