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The rapidly-developing intersection of machine learning (ML) with high-energy physics (HEP) presents both opportunities and challenges to our community.
Gauge Equivariant Convolutional Networks and the Icosahedral CNN ,
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ParticleNet: Jet Tagging via Particle Clouds ,
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Extending the search for new resonances with machine learning ,
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A robust anomaly finder based on autoencoders (2019),
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Adversarially-trained autoencoders for robust unsupervised new physics searches ,
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Parametrizing the Detector Response with Neural Networks ,
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Learning multivariate new physics ,
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ABCNet: An attention-based method for particle tagging ,
V. Mikuni and F. Canelli, · 2001
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Anomaly Detection with Density Estimation ,
B. Nachman and D. Shih, · 2001
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Simulation Assisted Likelihood-free Anomaly Detection ,
A. Andreassen, B. Nachman and D. Shih, · 2001
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J. Kieseler, · 2002
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Black-Box Optimization with Local Generative Surrogates ,
S. Shirobokov, V. Belavin, M. Kagan, A. Ustyuzhanin and A. G. Baydin, · 2002
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Fast inference of Boosted Decision Trees in FPGAs for particle physics ,
S. Summers et al. , · 2002
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Graph Neural Networks for Particle Reconstruction in High Energy Physics detectors ,
X. Ju et al. , · 2003
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Compressing deep neural networks on FPGAs to binary and ternary precision with HLS4ML ,
J. Ngadiuba et al. , · 2003
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Use of a generalized energy Mover’s distance in the search for rare phenomena at colliders ,
M. Crispim Romão, N. F. Castro, J. G. Milhano, R. Pedro and T. Vale, · 2004
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Adversarially Learned Anomaly Detection on CMS Open Data: re-discovering the top quark ,
O. Knapp, O. Cerri, G. Dissertori, T. Q. Nguyen, M. Pierini and J.-R. Vlimant, · 2005
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Lorentz Group Equivariant Neural Network for Particle Physics (2020),
A. Bogatskiy, B. Anderson, J. T. Offermann, M. Roussi, D. W. Miller and R. Kondor, · 2006
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Invertible Networks or Partons to Detector and Back Again ,
M. Bellagente, A. Butter, G. Kasieczka, T. Plehn, A. Rousselot, R. Winterhalder, L. Ardizzone and U. Köthe, · 2006
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M. Crispim Romão, N. F. Castro and R. Pedro, · 2006
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Dealing with Nuisance Parameters using Machine Learning in High Energy Physics: a Review (2020),
T. Dorigo and P. De Castro Manzano, · 2007
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Unsupervised Outlier Detection in Heavy-Ion Collisions ,
P. Thaprasop, K. Zhou, J. Steinheimer and C. Herold, · 2007
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C. K. Khosa and V. Sanz, · 2007
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Variational Autoencoders for Anomalous Jet Tagging (2020),
T. Cheng, J.-F. Arguin, J. Leissner-Martin, J. Pilette and T. Golling, · 2007
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Reconstructing boosted Higgs jets from event image segmentation (2020),
J. Li, T. Li and F.-Z. Xu, · 2008
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Sampling using S U ( N ) SU(N) gauge equivariant flows ,
D. Boyda, G. Kanwar, S. Racanière, D. J. Rezende, M. S. Albergo, K. Cranmer, D. C. Hackett and P. E. Shanahan, · 2008
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Jet Flavour Classification Using DeepJet (2020),
E. Bols, J. Kieseler, M. Verzetti, M. Stoye and A. Stakia, · 2008
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Secondary Vertex Finding in Jets with Neural Networks (2020),
J. Shlomi, S. Ganguly, E. Gross, K. Cranmer, Y. Lipman, H. Serviansky, H. Maron and N. Segol, · 2008
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Decoding Dark Matter Substructure without Supervision (2020),
S. Alexander, S. Gleyzer, H. Parul, P. Reddy, M. W. Toomey, E. Usai and R. Von Klar, · 2008
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Y. Iiyama et al. , · 2008
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HL-LHC Computing Review: Common Tools and Community Software ,
T. Aarrestad et al. , · 2008
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Measurement of the top quark mass with dilepton events selected using neuroevolution at CDF ,
T. Aaltonen et al. , · 2009
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An Attention Based Neural Network for Jet Tagging (2020),
J. Li and H. Sun, · 2009
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Disentangling Boosted Higgs Boson Production Modes with Machine Learning (2020),
Y.-L. Chung, S.-C. Hsu and B. Nachman, · 2009
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Simulation-assisted decorrelation for resonant anomaly detection ,
K. Benkendorfer, L. L. Pottier and B. Nachman, · 2009
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DCTRGAN: Improving the Precision of Generative Models with Reweighting ,
S. Diefenbacher, E. Eren, G. Kasieczka, A. Korol, B. Nachman and D. Shih, · 2009
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Mapping Machine-Learned Physics into a Human-Readable Space ,
T. Faucett, J. Thaler and D. Whiteson, · 2010
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Permutationless Many-Jet Event Reconstruction with Symmetry Preserving Attention Networks (2020),
M. J. Fenton, A. Shmakov, T.-W. Ho, S.-C. Hsu, D. Whiteson and P. Baldi, · 2010
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Anomaly Detection With Conditional Variational Autoencoders ,
A. A. Pol, V. Berger, G. Cerminara, C. Germain and M. Pierini, · 2010
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FPGAs-as-a-Service Toolkit (FaaST) ,
D. S. Rankin et al. , · 2010
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Learning to identify electrons ,
J. Collado, J. N. Howard, T. Faucett, T. Tong, P. Baldi and D. Whiteson, · 2011
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Explainable AI for ML jet taggers using expert variables and layerwise relevance propagation ,
G. Agarwal, L. Hay, I. Iashvili, B. Mannix, C. McLean, M. Morris, S. Rappoccio and U. Schubert, · 2011
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Explainable machine learning of the underlying physics of high-energy particle collisions (2020),
Y. S. Lai, D. Neill, M. Płoskoń and F. Ringer, · 2012
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Quark-Gluon Jet Discrimination Using Convolutional Neural Networks ,
J. S. H. Lee, I. Park, I. J. Watson and S. Yang, · 2012
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Deep learning jet modifications in heavy-ion collisions (2020),
Y.-L. Du, D. Pablos and K. Tywoniuk, · 2012
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P. Abratenko et al. , · 2012
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Lattice gauge equivariant convolutional neural networks (2020),
M. Favoni, A. Ipp, D. I. Müller and D. Schuh, · 2012
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Equivariant Energy Flow Networks for Jet Tagging (2020),
M. J. Dolan and A. Ore, · 2012
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Zero-Permutation Jet-Parton Assignment using a Self-Attention Network (2020),
J. S. H. Lee, I. Park, I. J. Watson and S. Yang, · 2012
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G. Stein, U. Seljak and B. Dai, · 2012
Cited alongside, same era.
How to GAN Higher Jet Resolution (2020),
P. Baldi, L. Blecher, A. Butter, J. Collado, J. N. Howard, F. Keilbach, T. Plehn, G. Kasieczka and D. Whiteson, · 2012
Cited alongside, same era.
Accelerated Charged Particle Tracking with Graph Neural Networks on FPGAs ,
A. Heintz et al. , · 2012
Cited alongside, same era.
Searching for Exotic Particles in High-Energy Physics with Deep Learning ,
P. Baldi, P. Sadowski and D. Whiteson, · 2014
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Jet-Images: Computer Vision Inspired Techniques for Jet Tagging ,
End-to-End Jet Classification of Boosted Top Quarks with the CMS Open Data (2021),
M. Andrews et al. , · 2021
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Jet Single Shot Detection (2021),
A. A. Pol et al. , · 2021
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MLPF: Efficient machine-learned particle-flow reconstruction using graph neural networks ,
J. Pata, J. Duarte, J.-R. Vlimant, M. Pierini and M. Spiropulu, · 2021
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Jet characterization in Heavy Ion Collisions by QCD-Aware Graph Neural Networks (2021),
Y. Verma and S. Jena, · 2021
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J. Cogan, M. Kagan, E. Strauss and A. Schwarztman, · 2015
Cited alongside, same era.
Playing Tag with ANN: Boosted Top Identification with Pattern Recognition ,
L. G. Almeida, M. Backović, M. Cliche, S. J. Lee and M. Perelstein, · 2015
Cited alongside, same era.
Deep Variational Information Bottleneck ,
A. A. Alemi, I. Fischer, J. V. Dillon and K. Murphy, · 2016
Cited alongside, same era.
M. Ribeiro, S. Singh and C. Guestrin (2016)
2016
Cited alongside, same era.
Learning to Pivot with Adversarial Networks (2016),
G. Louppe, M. Kagan and K. Cranmer, · 2016
Cited alongside, same era.
Parameterized neural networks for high-energy physics ,
P. Baldi, K. Cranmer, T. Faucett, P. Sadowski and D. Whiteson, · 2016
Cited alongside, same era.
Jet-images — deep learning edition ,
L. de Oliveira, M. Kagan, L. Mackey, B. Nachman and A. Schwartzman, · 2016
Cited alongside, same era.
Group equivariant convolutional networks ,
T. Cohen and M. Welling, · 2016
Cited alongside, same era.
D. Maître and H. Truong, · 2021
Later among the works it cites.
Generalization capabilities of translationally equivariant neural networks (2021),
S. Bulusu, M. Favoni, A. Ipp, D. I. Müller and D. Schuh, · 2021
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Development of a Vertex Finding Algorithm using Recurrent Neural Network (2021), arXiv:2101.11906
K. Goto, T. Suehara, T. Yoshioka, M. Kurata, H. Nagahara, Y. Nakashima, N. Takemura and M. Iwasaki, · 2021
Later among the works it cites.
Sequence-based Machine Learning Models in Jet Physics (2021),
R. T. de Lima, · 2021
Later among the works it cites.
Point Cloud Transformers applied to Collider Physics (2021),
V. Mikuni and F. Canelli, · 2021
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A. Shmakov, M. J. Fenton, T.-W. Ho, S.-C. Hsu, D. Whiteson and P. Baldi, · 2021
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Particle Convolution for High Energy Physics (2021), arXiv:2107.02908
C. Shimmin, · 2021
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Scalable, End-to-End, Deep-Learning-Based Data Reconstruction Chain for Particle Imaging Detectors ,
F. Drielsma, K. Terao, L. Dominé and D. H. Koh, · 2021
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P. Abratenko et al. , · 2021
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J. Hewes et al. , · 2021
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R. Abbasi et al. , · 2021
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Anomaly detection with convolutional Graph Neural Networks ,
O. Atkinson, A. Bhardwaj, C. Englert, V. S. Ngairangbam and M. Spannowsky, · 2021
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Autoencoders for unsupervised anomaly detection in high energy physics ,
T. Finke, M. Krämer, A. Morandini, A. Mück and I. Oleksiyuk, · 2021
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Event-based anomaly detection for new physics searches at the LHC using machine learning (2021),
S. V. Chekanov and W. Hopkins, · 2021
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The LHC Olympics 2020 a community challenge for anomaly detection in high energy physics ,
G. Kasieczka et al. , · 2021
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Deep molecular dreaming: inverse machine learning for de-novo molecular design and interpretability with surjective representations ,
C. Shen, M. Krenn, S. Eppel and A. Aspuru-Guzik, · 2021
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A graph placement methodology for fast chip design ,
A. Mirhoseini, A. Goldie, M. Yazgan, J. W. Jiang, E. Songhori, S. Wang, Y.-J. Lee, E. Johnson, O. Pathak, A. Nazi, J. Pak, A. Tong et al. , · 2021
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E. Govorkova et al. , · 2021
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Muon trigger with fast Neural Networks on FPGA, a demonstrator (2021),
M. Migliorini, J. Pazzini, A. Triossi, M. Zanetti and A. Zucchetta, · 2021
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T. M. Hong, B. T. Carlson, B. R. Eubanks, S. T. Racz, S. T. Roche, J. Stelzer and D. C. Stumpp, · 2021
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Fast convolutional neural networks on FPGAs with hls4ml (2021),
T. Aarrestad et al. , · 2021
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A reconfigurable neural network ASIC for detector front-end data compression at the HL-LHC (2021),
G. Di Guglielmo et al. , · 2021
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Resolving Extreme Jet Substructure (2022),
Y. Lu, A. Romero, M. J. Fenton, D. Whiteson and P. Baldi, · 2022
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Machine Learning and LHC Event Generation ,
A. Butter et al. , · 2022
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Symmetry Group Equivariant Architectures for Physics ,
A. Bogatskiy et al. , · 2022
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Explainable ai for high energy physics ,
M. S. Neubauer and A. Roy, · 2022
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A cautionary tale of decorrelating theory uncertainties ,
A. Ghosh and B. Nachman, · 2022
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Solving Simulation Systematics in and with AI/ML ,
B. Viren, J. Huang, Y. Huang, M. Lin, Y. Ren, K. Terao, D. Torbunov and H. Yu, · 2022
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Interpretable uncertainty quantification in ai for hep ,
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Applications of Machine Learning to Lattice Quantum Field Theory (2022), arXiv:2202.05838
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Machine Learning and Cosmology ,
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Learning to simulate high energy particle collisions from unlabeled data ,
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Creating Simple, Interpretable Anomaly Detectors for New Physics in Jet Substructure (2022),
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Challenges for unsupervised anomaly detection in particle physics ,
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Deep Set Auto Encoders for Anomaly Detection in Particle Physics ,
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Detecting New Physics as Novelty – Complementarity Matters (2022),
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Classifying anomalies through outer density estimation ,
A. Hallin, J. Isaacson, G. Kasieczka, C. Krause, B. Nachman, T. Quadfasel, M. Schlaffer, D. Shih and M. Sommerhalder, · 2022
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T. Dorigo et al. , · 2022
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A. Scheinker and S. Gessner, · 2022
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Innovations in trigger and data acquisition systems for next-generation physics facilities ,
R. Bartoldus, C. Bernius and D. W. Miller, · 2022
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Deep learning applications for quality control in particle detector construction (2022),
N. Akchurin, J. Damgov, S. Dugad, P. G. C, S. Grönroos, K. Lamichhane, J. Martinez, T. Quast, S. Undleeb and A. Whitbeck, · 2022
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Physics Community Needs, Tools, and Resources for Machine Learning ,
P. Harris et al. , · 2022
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Software and Computing for Small HEP Experiments ,
C. Andreopoulos et al. , · 2022
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Y. Kahn et al. , · 2022
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Dark-matter And Neutrino Computation Explored (DANCE) Community Input to Snowmass ,
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Hep computing collaborations for the challenges of the next decade ,
S. Campana, A. Di Girolamo, P. Laycock, Z. Marshall, H. Schellman and G. A. Stewart, · 2022
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Data Science and Machine Learning in Education ,
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How to tell quark jets from gluon jets ,
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