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The Jiangmen Underground Neutrino Observatory (JUNO) is an experiment designed to study neutrino oscillations.
“The perceptron: a probabilistic model for information storage and organization in the brain.”
Frank Rosenblatt · 1958
Earlier work this paper cites.
“Density Estimation for Statistics and Data Analysis”, Chapman & Hall/CRC Monographs on Statistics & Applied Probability
B.W. Silverman · 1986
Earlier work this paper cites.
“Simplifying Decision Trees”
Ross Quinlan · 1987
Earlier work this paper cites.
“Learning Representations by Back-Propagating Errors”
David. Rumelhart · 1988
Earlier work this paper cites.
“Greedy Function Approximation: A Gradient Boosting Machine”
Jerome Friedman · 2001
Earlier work this paper cites.
“Random Forests”
Leo Breiman · 2001
Earlier work this paper cites.
“Stochastic gradient boosting”
Jerome. Friedman · 2002
Earlier work this paper cites.
“Greedy Layer-Wise Training of Deep Networks”
Yoshua Bengio · 2006
Earlier work this paper cites.
“Graph Neural Networks in TensorFlow and Keras with Spektral”, 2020
Daniele Grattarola and Cesare Alippi · 2006
Earlier work this paper cites.
“Time and space reconstruction in optical, non-imaging, scintillator-based particle detectors”
Cristiano Galbiati and Kevin McCarty · 2006
Earlier work this paper cites.
“Weighted Graph Cuts without Eigenvectors A Multilevel Approach”
I. Dhillon · 2007
Earlier work this paper cites.
“Large-Scale Deep Unsupervised Learning Using Graphics Processors”
Rajat Raina · 2009
Earlier work this paper cites.
“Calibration Strategy of the JUNO Experiment”, 2020
Angel Abusleme · 2011
Earlier work this paper cites.
“Scikit-learn: Machine Learning in Python”
Fabian Pedregosa · 2011
Earlier work this paper cites.
“A method of sharing dynamic geometry information to study liquid-based detectors”, 2020
Shu Zhang · 2012
Earlier work this paper cites.
“ImageNet Classification with Deep Convolutional Neural Networks”
Alex Krizhevsky · 2012
Earlier work this paper cites.
“Searching for Exotic Particles in High-Energy Physics with Deep Learning”
Pierre Baldi · 2014
Earlier work this paper cites.
“Going Deeper with Convolutions”, 2014
Christian Szegedy · 2014
Earlier work this paper cites.
“Convolutional Neural Networks at Constrained Time Cost”, 2014
Kai He and Jian Sun · 2014
Earlier work this paper cites.
“JUNO Conceptual Design Report”, 2015
Thomas Adam · 2015
Cited alongside, same era.
“Multivariate Density Estimation: Theory, Practice, and Visualization”, Wiley Series in Probability and Statistics
D.W. Scott · 2015
Cited alongside, same era.
“Very Deep Convolutional Networks for Large-Scale Image Recognition”, 2015
Karen Simonyan and Andrew Zisserman · 2015
Cited alongside, same era.
“Deep Residual Learning for Image Recognition”, 2015
Kai He · 2015
Cited alongside, same era.
“TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems” Software available from tensorflow.org, 2015
Martin Adabi · 2015
Cited alongside, same era.
“A Unified Approach to Interpreting Model Predictions”, 2017
Scott Lundberg and Su-In Lee · 2017
Later among the works it cites.
“Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review”
Waseem Rawat and Zeng Wang · 2017
Later among the works it cites.
“Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering”, 2017
Micha“”el Defferrard · 2017
Later among the works it cites.
“Adversarial Discriminative Domain Adaptation”, 2017
Eric Tzeng · 2017
Later among the works it cites.
“Deep Learning and its Application to LHC Physics”
Dan Guest · 2018
Later among the works it cites.
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“Unsupervised Domain Adaptation by Backpropagation”
Yaroslav Ganin and Victor Lempitsky · 2015
Cited alongside, same era.
“Neutrino Physics with JUNO”
Feng An · 2016
Cited alongside, same era.
“Jet Flavor Classification in High-Energy Physics with Deep Neural Networks”
Daniel Guest · 2016
Cited alongside, same era.
“Recent developments in Geant4”
J. Allison · 2016
Cited alongside, same era.
“XGBoost: A Scalable Tree Boosting System”, 2016
Tianqi Chen and Carlos Guestrin · 2016
Cited alongside, same era.
“Multilayer Perceptron (MLP)”
Petar Veli“ˆckovi“’c · 2016
Cited alongside, same era.
Jimmy Ba · 2016
Cited alongside, same era.
“GDML based geometry management system for offline software in JUNO”
Kai Li · 2018
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“A ROOT Based Event Display Software for JUNO”
Zheng You · 2018
Later among the works it cites.
Leslie. Smith · 2018
Later among the works it cites.
“Stochastic Gradient Descent as Approximate Bayesian Inference”, 2018
Stephan Mandt · 2018
Later among the works it cites.
“The HEALPix Primer”, 2018
K.. Górski et al · 2018
Later among the works it cites.
“IT‑ecosystem of the HybriLIT heterogeneous platform for high‑performance computing and training of IT‑specialists”
Gh. Adam · 2018
Later among the works it cites.
“A method of detector and event visualization with Unity in JUNO”
Jiang Zhu · 2019
Later among the works it cites.
“Deep Learning using Rectified Linear Units (ReLU)”, 2019
Abien Agarap · 2019
Later among the works it cites.
“A Sufficient Condition for Convergences of Adam and RMSProp”, 2019
Fang Zou · 2019
Later among the works it cites.
“Control Batch Size and Learning Rate to Generalize Well: Theoretical and Empirical Evidence”
Feng He · 2019
Later among the works it cites.
“DeepSphere: Efficient spherical Convolutional Neural Network with HEALPix sampling for cosmological applications”
Nathana“”el Perraudin · 2019
Later among the works it cites.
“Accelerating dark matter search in emulsion SHiP detector by deep learning”
S.K. Shirobokov · 2020
Later among the works it cites.
“Tested Performance of JUNO 20” PMTs”
Hai Zhang · 2020
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