Fetching the paper…
Reading the bibliography…
High-power particle accelerators are complex machines with thousands of pieces of equipmentthat are frequently running at the cutting edge of technology.
SciPy: Open source scientific tools for Python, 2001–
Eric Jones, Travis Oliphant, Pearu Peterson, et al · 2001
Earlier work this paper cites.
Dimensionality reduction by learning an invariant mapping
R. Hadsell, S. Chopra, and Y. LeCun · 2006
Earlier work this paper cites.
A new differential and errant beam current monitor for the sns* accelerator
Willem Blokland and Charles C Peters · 2013
Earlier work this paper cites.
The spallation neutron source accelerator system design
S. Henderson, W. Abraham, A. Aleksandrov, C. Allen, J. Alonso, D. Anderson, D. Arenius, T. Arthur, S. Assadi, J. Ayers, P. Bach, V. Badea, R. Battle, J. Beebe-Wang, B. Bergmann, and others · 2014
Earlier work this paper cites.
Siamese neural networks for one-shot image recognition
Gregory Koch, Richard Zemel, Ruslan Salakhutdinov, et al · 2015
Earlier work this paper cites.
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, and others · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, X. Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Learning deep features for discriminative localization
Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba · 2016
Cited alongside, same era.
Accurate prediction of x-ray pulse properties from a free-electron laser using machine learning
A. Sanchez-Gonzalez, P. Micaelli, C. Olivier, T. R. Barillot, M. Ilchen, A. A. Lutman, A. Marinelli, T. Maxwell, A. Achner, M. Agåker, N. Berrah, C. Bostedt, J. D. Bozek, J. Buck, P. H. Bucksbaum, et al · 2017
Cited alongside, same era.
Machine learning-based longitudinal phase space prediction of particle accelerators
C. Emma, A. Edelen, M. J. Hogan, B. O’Shea, G. White, and V. Yakimenko · 2018
Cited alongside, same era.
Application of machine learning to beam diagnostics
E. Fol, J. C. de Portugal, and R. Tomas · 2018
Cited alongside, same era.
Machine learning applied at the LHC for beam loss pattern classification
Gianluca Valentino and Belen Salvachua · 2018
Cited alongside, same era.
Enhancements to the SNS* Differential Current Monitor to Minimize Errant Beam
Willem Blokland, Charles Peters, and Tim Southern · 2019
Later among the works it cites.
Detection of faulty beam position monitors using unsupervised learning
E. Fol, R. Tomás, J. Coello de Portugal, and G. Franchetti · 2020
Later among the works it cites.
Superconducting radio-frequency cavity fault classification using machine learning at jefferson laboratory
Chris Tennant, Adam Carpenter, Tom Powers, Anna Shabalina Solopova, Lasitha Vidyaratne, and Khan Iftekharuddin · 2020
Later among the works it cites.
Predicting particle accelerator failures using binary classifiers
Miha Rescic, Rebecca Seviour, and Willem Blokland · 2020
Later among the works it cites.
Simple and principled uncertainty estimation with deterministic deep learning via distance awareness, 2020
Jeremiah Zhe Liu, Zi Lin, Shreyas Padhy, Dustin Tran, Tania Bedrax-Weiss, and Balaji Lakshminarayanan · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Demonstration of machine learning-based model-independent stabilization of source properties in synchrotron light sources
S. C. Leemann, S. Liu, A. Hexemer, M. A. Marcus, C. N. Melton, H. Nishimura, and C. Sun · 2019
Cited alongside, same era.
Unsupervised machine learning for detection of faulty beam position monitors
E. Fol and R. Tomas J. C. de Portugal · 2019
Cited alongside, same era.
Using neural networks with data quantization for time series analysis in LHC superconducting magnets
Maciej Wielgosz and Andrzej Skoczeń · 2019
Cited alongside, same era.
A novel approach for classification and forecasting of time series in particle accelerators
Sichen Li, Mélissa Zacharias, Jochem Snuverink, Jaime Coello de Portugal, Fernando Perez-Cruz, Davide Reggiani, and Andreas Adelmann · 2021
Closest in time.
https://www.cpubenchmark.net/compare/
Cpu benchmark · 2021
Closest in time.