Fetching the paper…
Reading the bibliography…
Constraining the parameters of physical models with $>5-10$ parameters is a widespread problem in fields like particle physics and astronomy.
Advances in neural information processing systems 2
N. Morgan and H. Bourlard · 1990
Earlier work this paper cites.
Query by committee
H. S. Seung, M. Opper, and H. Sompolinsky · 1992
Earlier work this paper cites.
Improving generalization with active learning
David Cohn, Les Atlas, and Richard Ladner · 1994
Earlier work this paper cites.
Bagging Predictors
Breiman, L · 1996
Earlier work this paper cites.
A Supersymmetry primer
Stephen P. Martin · 1998
Earlier work this paper cites.
Random forests
Leo Breiman · 2001
Earlier work this paper cites.
SOFTSUSY: a program for calculating supersymmetric spectra
B. C. Allanach · 2002
Earlier work this paper cites.
Gaussian Processes for Machine Learning
Carl Edward Rasmussen and Christopher K. I. Williams · 2006
Earlier work this paper cites.
Active learning for logistic regression: an evaluation
Andrew I. Schein and Lyle H. Ungar · 2007
Earlier work this paper cites.
Scalable parallel programming with cuda
John Nickolls, Ian Buck, Michael Garland, and Kevin Skadron · 2008
Cited alongside, same era.
Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay · 2011
Cited alongside, same era.
Active learning
Burr Settles · 2012
Cited alongside, same era.
Adam: A Method for Stochastic Optimization
D. P. Kingma and J. Ba · 2014
Cited alongside, same era.
Dropout: A simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
Cited alongside, same era.
Summary of the ATLAS experiment’s sensitivity to supersymmetry after LHC Run 1 — interpreted in the phenomenological MSSM
Georges Aad et al · 2015
Summary of the atlas experiment’s sensitivity to supersymmetry after lhc run 1 — interpreted in the phenomenological mssm
The ATLAS collaboration · 2015
Later among the works it cites.
First interpretation of 13 TeV supersymmetry searches in the pMSSM
Alan Barr and Jesse Liu · 2016
Later among the works it cites.
The BSM-AI project: SUSY-AI–generalizing LHC limits on supersymmetry with machine learning
Sascha Caron, Jong Soo Kim, Krzysztof Rolbiecki, Roberto Ruiz de Austri, and Bob Stienen · 2017
Later among the works it cites.
What uncertainties do we need in bayesian deep learning for computer vision?
Alex Kendall and Yarin Gal · 2017
Later among the works it cites.
GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration
Jacob R. Gardner, Geoff Pleiss, David Bindel, Kilian Q. Weinberger, and Andrew Gordon Wilson · 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.
TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dandelion Mané, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng · 2015
Cited alongside, same era.
QBDC: Query by dropout committee for training deep supervised architecture
Melanie Ducoffe and Frederic Precioso · 2015
Cited alongside, same era.
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal and Z. Ghahramani · 2015
Cited alongside, same era.
Remus Pop and Patric Fulop · 2018
Later among the works it cites.
Dropout-based active learning for regression
Evgenii Tsymbalov, Maxim Panov, and Alexander Shapeev · 2018
Later among the works it cites.
”levelset estimation with bayesian optimisation”
K. Cranmer, L. Heinrich, and G. Louppe · 2019
Closest in time.