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The observation of resonances is unequivocal evidence of new physics beyond the Standard Model at the Large Hadron Collider (LHC).
1901
Earlier work this paper cites.
1909
Earlier work this paper cites.
1912
Earlier work this paper cites.
P. W. Higgs, Broken symmetries, massless particles and gauge fields
1964
Earlier work this paper cites.
F. Englert and R. Brout, Broken symmetry and the mass of gauge vector mesons
1964
Earlier work this paper cites.
P. W. Higgs, Broken symmetries and the masses of gauge bosons
1964
Earlier work this paper cites.
G. S. Guralnik, C. R. Hagen, and T. W. Kibble, Global conservation laws and massless particles
1964
Earlier work this paper cites.
B. M. Shahshahani and D. A. Landgrebe, The effect of unlabeled samples in reducing the small sample size problem and mitigating the hughes phenomenon
1994
Earlier work this paper cites.
G. Towell, Using unlabeled data for supervised learning
1996
Earlier work this paper cites.
R. Brun and F. Rademakers, ROOT: An object oriented data analysis framework
1997
Earlier work this paper cites.
A. Blum and T. Mitchell, Combining labeled and unlabeled data with co-training
1998
Earlier work this paper cites.
S. Baluja, Probabilistic modeling for face orientation discrimination: Learning from labeled and unlabeled data
1999
Earlier work this paper cites.
C. Fyfe, Artificial neural networks and information theory
2000
Earlier work this paper cites.
S. Goldman and Y. Zhou, Enhancing supervised learning with unlabeled data
2000
Earlier work this paper cites.
V. Abazov, B. Abbott, A. Abdesselam, M. Abolins, V. Abramov, B. Acharya, D. Adams, M. Adams, S. Ahmed, G. Alexeev, et al., Search for single top quark production at dø using neural networks
2001
Earlier work this paper cites.
K. P. Nigam, Using unlabeled data to improve text classification
2001
Earlier work this paper cites.
D. Whiteson and N. Naumann, Support vector regression as a signal discriminator in high energy physics
2003
Earlier work this paper cites.
D. Tao, X. Li, W. Hu, S. Maybank, and X. Wu, Supervised tensor learning
2005
Earlier work this paper cites.
D. Acosta, J. Adelman, T. Affolder, T. Akimoto, M. Albrow, D. Ambrose, S. Amerio, D. Amidei, A. Anastassov, K. Anikeev, et al., Measurement of the cross section for t t production in p p collisions using the kinematics of lepton+ jets events
2005
Earlier work this paper cites.
D. J. Crandall and D. P. Huttenlocher, Weakly supervised learning of part-based spatial models for visual object recognition
2006
Earlier work this paper cites.
K.-F. Wong, M. Wu, and W. Li, Extractive summarization using supervised and semi-supervised learning
2008
Earlier work this paper cites.
M. Cacciari, G. P. Salam, and G. Soyez, The anti- k t k_{t} jet clustering algorithm
2008
Earlier work this paper cites.
2009
Cited alongside, same era.
Y. Bengio, Learning deep architectures for ai
2009
Cited alongside, same era.
S. Whiteson and D. Whiteson, Machine learning for event selection in high energy physics
2009
Cited alongside, same era.
X. Glorot and Y. Bengio, Understanding the difficulty of training deep feedforward neural networks
2010
Cited alongside, same era.
P. Siva and T. Xiang, Weakly supervised object detector learning with model drift detection
2011
Cited alongside, same era.
G. Patrini, F. Nielsen, R. Nock, and M. Carioni, Loss factorization, weakly supervised learning and label noise robustness
2016
Later among the works it cites.
F. Zhang, B. Du, L. Zhang, and M. Xu, Weakly supervised learning based on coupled convolutional neural networks for aircraft detection
2016
Later among the works it cites.
D.-A. Huang, L. Fei-Fei, and J. C. Niebles, Connectionist temporal modeling for weakly supervised action labeling
2016
Later among the works it cites.
X. Yao, J. Han, G. Cheng, X. Qian, and L. Guo, Semantic annotation of high-resolution satellite images via weakly supervised learning
2016
Later among the works it cites.
2017
Later among the works it cites.
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2011
Cited alongside, same era.
G. Hartmann, M. Grundmann, J. Hoffman, D. Tsai, V. Kwatra, O. Madani, S. Vijayanarasimhan, I. Essa, J. Rehg, and R. Sukthankar, Weakly supervised learning of object segmentations from web-scale video
2012
Cited alongside, same era.
M. Cacciari, G. P. Salam, and G. Soyez, FastJet User Manual
2012
Cited alongside, same era.
2013
Cited alongside, same era.
2013
Cited alongside, same era.
C. Szegedy, A. Toshev, and D. Erhan, Deep neural networks for object detection
2013
Cited alongside, same era.
R. D. Ball et al., Parton distributions with LHC data
2013
Cited alongside, same era.
F. Carminati, G. Khattak, M. Pierini, S. Vallecor-safa, and A. Farbin, Calorimetry with deep learning: particle classification, energy regression, and simulation for high-energy physics
2017
Later among the works it cites.
L. M. Dery, B. Nachman, F. Rubbo, and A. Schwartzman, Weakly supervised classification in high energy physics
2017
Later among the works it cites.
M. Dehghani, H. Zamani, A. Severyn, J. Kamps, and W. B. Croft, Neural ranking models with weak supervision
2017
Later among the works it cites.
H. Kuehne, A. Richard, and J. Gall, Weakly supervised learning of actions from transcripts
2017
Later among the works it cites.
2018
Later among the works it cites.
T. Cohen, M. Freytsis, and B. Ostdiek, (machine) learning to do more with less
2018
Later among the works it cites.
K. Albertsson, P. Altoe, D. Anderson, M. Andrews, J. P. A. Espinosa, A. Aurisano, L. Basara, A. Bevan, W. Bhimji, D. Bonacorsi, et al., Machine learning in high energy physics community white paper
2018
Later among the works it cites.
D. Guest, K. Cranmer, and D. Whiteson, Deep learning and its application to LHC physics
2018
Later among the works it cites.
J. Choma, Ensembles of neural networks for time series with application to climate change prediction
2018
Later among the works it cites.
G. Lee, J. Jeong, S. Seo, C. Kim, and P. Kang, Sentiment classification with word localization based on weakly supervised learning with a convolutional neural network
2018
Later among the works it cites.
Z.-H. Zhou, A brief introduction to weakly supervised learning
2018
Later among the works it cites.
H. Zamani and W. B. Croft, On the theory of weak supervision for information retrieval
2018
Later among the works it cites.
2019
Later among the works it cites.
M. Abdughani, J. Ren, L. Wu, J.-M. Yang, and J. Zhao, Supervised deep learning in high energy phenomenology: a mini review
2019
Later among the works it cites.
C. Chen, Y. Liu, M. Kumar, J. Qin, and Y. Ren, Energy consumption modelling using deep learning embedded semi-supervised learning
2019
Later among the works it cites.
Y. Wang, S. Sohn, S. Liu, F. Shen, L. Wang, E. J. Atkinson, S. Amin, and H. Liu, A clinical text classification paradigm using weak supervision and deep representation
2019
Later among the works it cites.