المؤلفون: Cécile Germain, Maurizio Pierini, Gianluca Carminara, Adrian Alan Pol

مصطلحات موضوعية: 010308 nuclear & particles physics, Computer science, Data quality, 0103 physical sciences, Anomaly detection, 010306 general physics, 01 natural sciences, Remote sensing

URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_________::3616e12e233b11978e81dd3ad1f2b052
https://doi.org/10.1142/12200

المؤلفون: Adrian Alan Pol, Thea Aarrestad, Ekaterina Govorkova, Roi Halily, Anat Klempner, Tal Kopetz, Vladimir Loncar, Jennifer Ngadiuba, Maurizio Pierini, Olya Sirkin, Sioni Summers

المصدر: Machine Learning: Science and Technology, 3 (2)

مصطلحات موضوعية: FOS: Computer and information sciences, Computer Science - Machine Learning, hep-ex, jet images, cs.LG, FOS: Physical sciences, quantization aware training, object detection, High Energy Physics - Experiment, Machine Learning (cs.LG), high energy physics, Computing and Computers, Human-Computer Interaction, High Energy Physics - Experiment (hep-ex), Artificial Intelligence, jet tagging, jet reconstruction, attention mechanism, Software, Particle Physics - Experiment

وصف الملف: application/application/pdf

URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d433a2ab5a491d8f332742decb47725c
http://cds.cern.ch/record/2801371

المؤلفون: Ekaterina Govorkova, Ema Puljak, Thea Aarrestad, Thomas James, Vladimir Loncar, Maurizio Pierini, Adrian Alan Pol, Nicolò Ghielmetti, Maksymilian Graczyk, Sioni Summers, Jennifer Ngadiuba, Thong Q. Nguyen, Javier Duarte, Zhenbin Wu

مصطلحات موضوعية: Human-Computer Interaction, Physics::Instrumentation and Detectors, Artificial Intelligence, Computer Networks and Communications, hep-ex, Physics::Accelerator Physics, High Energy Physics::Experiment, Computer Vision and Pattern Recognition, Detectors and Experimental Techniques, physics.ins-det, Software, Particle Physics - Experiment

وصف الملف: application/pdf; image/jpeg

URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d13d8cc6e26d0c67c4ca4433d085f267
http://cds.cern.ch/record/2779339

المؤلفون: Adrian Alan Pol, Gianluca Cerminara, Cecile Germain, Maurizio Pierini

المساهمون: European Organization for Nuclear Research (CERN), Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), TAckling the Underspecified (TAU), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)

المصدر: Artificial Intelligence for High Energy Physics
Artificial Intelligence for High Energy Physics, In press, ⟨10.1142/12200⟩
Artificial Intelligence for High Energy Physics, World Scientific, 2022, ⟨10.1142/9789811234033_0004⟩

مصطلحات موضوعية: [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex], ComputingMilieux_MISCELLANEOUS, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]

URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::117d4385caaeb869ae09648d99defc66
https://hal.inria.fr/hal-03159873/file/IJMPA_Book__New_Version_.pdf

المؤلفون: Ekaterina Govorkova, Ema Puljak, Thea Aarrestad, Thomas James, Vladimir Loncar, Maurizio Pierini, Adrian Alan Pol, Nicolò Ghielmetti, Maksymilian Graczyk, Sioni Summers, Jennifer Ngadiuba, Thong Q. Nguyen, Javier Duarte, Zhenbin Wu

المصدر: Nature Machine Intelligence. 4:414-414

مصطلحات موضوعية: Human-Computer Interaction, Artificial Intelligence, Computer Networks and Communications, Computer Vision and Pattern Recognition, Software

URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_________::ac1498da2702a4d6dc007655d1a9b363
https://doi.org/10.1038/s42256-022-00486-4

المؤلفون: vloncar, Sioni Summers, Javier Duarte, Nhan Tran, Ben Kreis, jngadiub, Nicolò Ghielmetti, Duc Hoang, EJ Kreinar, Kelvin Lin, Maksymilian Graczyk, Adrian Alan Pol, ngpaladi, Dejan Golubovic, Yutaro Iiyama, Zhenbin Wu, Delon, Paolo Cretaro, veyron8800, Anders Wind, David, GDG, Jovan Mitrevski, Konstantin Vinogradov, Petr Zejdl, Sarun Nuntaviriyakul, Thea Aarrestad, drankincms

Relation: https://arxiv.org/abs/arXiv:1804.06913; https://doi.org/10.1088/1748-0221/13/07/P07027; https://doi.org/10.5281/zenodo.1201549; https://doi.org/10.5281/zenodo.5680908; oai:zenodo.org:5680908; https://github.com/fastmachinelearning/hls4ml/tree/v0.6.0

الاتاحة: https://doi.org/10.5281/zenodo.5680908
https://github.com/fastmachinelearning/hls4ml/tree/v0.6.0

المؤلفون: Adrian Alan Pol, Maurizio Pierini

Relation: https://zenodo.org/communities/mpp-hep; https://zenodo.org/communities/eu; https://doi.org/10.5281/zenodo.4883650; https://doi.org/10.5281/zenodo.4883651; oai:zenodo.org:4883651

الاتاحة: https://doi.org/10.5281/zenodo.4883651

المؤلفون: vloncar, thesps, Nhan Tran, Ben Kreis, Javier Duarte, jngadiub, Duc Hoang, EJ Kreinar, Adrian Alan Pol, Dejan Golubovic, Yutaro Iiyama, ngpaladi, Zhenbin Wu, Delon, Paolo Cretaro, veyron8800, Petr Zejdl, drankincms

Relation: https://github.com/fastmachinelearning/hls4ml/tree/v0.5.0-beta; https://doi.org/10.5281/zenodo.1201549; https://doi.org/10.5281/zenodo.4447439; oai:zenodo.org:4447439

الاتاحة: https://doi.org/10.5281/zenodo.4447439

المؤلفون: vloncar, thesps, Nhan Tran, Ben Kreis, Javier Duarte, jngadiub, Duc Hoang, EJ Kreinar, Adrian Alan Pol, ngpaladi, Dejan Golubovic, Yutaro Iiyama, Zhenbin Wu, Delon, Paolo Cretaro, veyron8800, Petr Zejdl, drankincms

Relation: https://github.com/fastmachinelearning/hls4ml/tree/v0.5.0; https://doi.org/10.5281/zenodo.1201549; https://doi.org/10.5281/zenodo.4585796; oai:zenodo.org:4585796

الاتاحة: https://doi.org/10.5281/zenodo.4585796

المؤلفون: Victor Berger, Cécile Germain, Adrian Alan Pol, Gianluca Cerminara, Maurizio Pierini

المساهمون: European Organization for Nuclear Research (CERN), TAckling the Underspecified (TAU), Laboratoire de Recherche en Informatique (LRI), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), CERN This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement no 772369)., Germain, Cecile

المصدر: ICMLA 2019-18th IEEE International Conference on Machine Learning and Applications
ICMLA 2019-18th IEEE International Conference on Machine Learning and Applications, Dec 2019, Boca Raton, United States
2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)
Eighteenth International Conference on Machine Learning and Applications
Eighteenth International Conference on Machine Learning and Applications, Dec 2019, Boca Raton, United States
ICMLA

مصطلحات موضوعية: [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], FOS: Computer and information sciences, Computer Science - Machine Learning, CERN Lab, Computer science, cs.LG, anomaly, FOS: Physical sciences, 02 engineering and technology, Anomaly detection, Variational autoencoder, 010501 environmental sciences, Machine learning, computer.software_genre, 01 natural sciences, Machine Learning (cs.LG), High Energy Physics - Experiment, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Bayes' theorem, High Energy Physics - Experiment (hep-ex), benchmark, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Component (UML), 0202 electrical engineering, electronic engineering, information engineering, [INFO]Computer Science [cs], 0105 earth and related environmental sciences, hep-ex, business.industry, variational, [INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG], Function (mathematics), Autoencoder, Computing and Computers, CERN LHC Coll, Metric (mathematics), 020201 artificial intelligence & image processing, Artificial intelligence, Anomaly (physics), business, computer, Particle Physics - Experiment, performance, trigger: monitoring

وصف الملف: application/pdf

URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a1b0a5f47d7765c9cdf3ddc47860d194
https://inria.hal.science/hal-02396279

المؤلفون: Giovanni Franzoni, Adrian Alan Pol, Federico de Guio, Maurizio Pierini, Jean-Roch Vlimant, Virginia Azzolini, Gianluca Cerminara, Filip Siroký

المساهمون: Université Paris-Saclay, CMS

المصدر: EPJ Web Conf.
23rd International Conference on Computing in High Energy and Nuclear Physics
23rd International Conference on Computing in High Energy and Nuclear Physics, Jul 2018, Sofia, Bulgaria. pp.06008, ⟨10.1051/epjconf/201921406008⟩
EPJ Web of Conferences, Vol 214, p 06008 (2019)

مصطلحات موضوعية: p p: scattering, data acquisition, neural network, QC1-999, media_common.quotation_subject, anomaly, Certification, quality: monitoring, Machine learning, computer.software_genre, 01 natural sciences, talk: Sofia 2018/07/09, Data acquisition, 0103 physical sciences, [PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex], Quality (business), [INFO]Computer Science [cs], Detectors and Experimental Techniques, 010306 general physics, Interpretability, media_common, Artificial neural network, 010308 nuclear & particles physics, business.industry, CMS, Physics, CERN LHC Coll, Data quality, Feedforward neural network, Anomaly detection, Artificial intelligence, business, computer, p p: colliding beams

وصف الملف: application/pdf

URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::33db660b49aa633819eb51dfa17ebb0f
https://resolver.caltech.edu/CaltechAUTHORS:20200310-081419478

المؤلفون: Adrian Alan Pol, Gianluca Cerminara, Virginia Azzolin, Maurizio Pierini, Jean-Roch Vlimant, Nancy Marinelli, Colin Jessop, Michael Benjamin Andrews, Nabarun Dev, Tanmay Mudholkar

المساهمون: Université Paris-Saclay, CMS

المصدر: EPJ Web Conf.
23rd International Conference on Computing in High Energy and Nuclear Physics
23rd International Conference on Computing in High Energy and Nuclear Physics, Jul 2018, Sofia, Bulgaria. pp.01007, ⟨10.1051/epjconf/201921401007⟩
EPJ Web of Conferences, Vol 214, p 01007 (2019)

مصطلحات موضوعية: Online and offline, business.industry, Physics, QC1-999, media_common.quotation_subject, Usability, Data loss, Benchmarking, Data science, Computing and Computers, 030218 nuclear medicine & medical imaging, Metadata, 03 medical and health sciences, 0302 clinical medicine, Workflow, Data quality, [PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex], Quality (business), [INFO]Computer Science [cs], [PHYS.PHYS.PHYS-INS-DET]Physics [physics]/Physics [physics]/Instrumentation and Detectors [physics.ins-det], Detectors and Experimental Techniques, business, Particle Physics - Experiment, 030217 neurology & neurosurgery, media_common

وصف الملف: application/pdf

URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2ea9c263868f18438d8d23ec0e15d543
https://hal.archives-ouvertes.fr/hal-02383388

المؤلفون: Hao Zhuang, Thea Klaeboe Aarrestad, Maurizio Pierini, Sioni Summers, Vladimir Loncar, Li Shan, Aki Kuusela, Claudionor Coelho, Jennifer Ngadiuba, Adrian Alan Pol

المصدر: Nature Machine Intelligence. 3(8):675-686

مصطلحات موضوعية: Signal Processing (eess.SP), FOS: Computer and information sciences, 0301 basic medicine, Computer Science - Machine Learning, Physics - Instrumentation and Detectors, Computer Networks and Communications, Computer science, cs.LG, FOS: Physical sciences, Inference, High Energy Physics - Experiment, Machine Learning (cs.LG), Reduction (complexity), High Energy Physics - Experiment (hep-ex), 03 medical and health sciences, Quantization (physics), 0302 clinical medicine, Artificial Intelligence, Gate array, FOS: Electrical engineering, electronic engineering, information engineering, Electrical Engineering and Systems Science - Signal Processing, Detectors and Experimental Techniques, Latency (engineering), physics.ins-det, Artificial neural network, hep-ex, business.industry, Deep learning, Image and Video Processing (eess.IV), eess.SP, Instrumentation and Detectors (physics.ins-det), Energy consumption, Electrical Engineering and Systems Science - Image and Video Processing, Computing and Computers, Human-Computer Interaction, 030104 developmental biology, eess.IV, Computer Vision and Pattern Recognition, Artificial intelligence, business, Algorithm, Particle Physics - Experiment, 030217 neurology & neurosurgery, Software

وصف الملف: application/pdf; image/jpeg

URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9b1ff33590a0ab83d546be5f64ed260f

المؤلفون: Maurizio Pierini, Agrima Seth, G. Cerminara, Adrian Alan Pol, Cécile Germain

المساهمون: European Organization for Nuclear Research (CERN), TAckling the Underspecified (TAU), Laboratoire de Recherche en Informatique (LRI), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université Paris-Saclay, This project has received fundingfrom the European Research Council (ERC) under theEuropean Union’s Horizon 2020 research and innova-tion program (grant agreement no 772369), CMS, CentraleSupélec-Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France

المصدر: Computing and Software for Big Science
Computing and Software for Big Science, 2019, 3 (1), ⟨10.1007/s41781-018-0020-1⟩
Computing and Software for Big Science, Springer, 2019, 3 (1), ⟨10.1007/s41781-018-0020-1⟩

مصطلحات موضوعية: FOS: Computer and information sciences, Artificial Neural Network, Computer Science - Machine Learning, Nuclear and High Energy Physics, Physics - Instrumentation and Detectors, Compact Muon Solenoid, Real-time computing, cs.LG, FOS: Physical sciences, Machine Learning (stat.ML), 02 engineering and technology, 01 natural sciences, Machine Learning (cs.LG), [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], physics.data-an, Machine Learning, Data Quality Monitoring, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Statistics - Machine Learning, 0103 physical sciences, 0202 electrical engineering, electronic engineering, information engineering, Computer Science (miscellaneous), [PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex], High Energy Physics, Detectors and Experimental Techniques, Data Analysis and Statistics, physics.ins-det, Artificial Neural Networks, Large Hadron Collider, Artificial neural network, 010308 nuclear & particles physics, business.industry, Detector, Instrumentation and Detectors (physics.ins-det), Collision, Automation, stat.ML, Computing and Computers, Physics - Data Analysis, Statistics and Probability, Data quality, 020201 artificial intelligence & image processing, Anomaly detection, Other, business, Data Analysis, Statistics and Probability (physics.data-an), Software

URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4eeb99d1f7c1fba0d043662fa4825d2e

المؤلفون: 'Adrian Alan Pol

المصدر: Thea Klaeboe Aarrestad

URL الوصول: https://explore.openaire.eu/search/publication?articleId=r3c4b2081b22::a19b6525e65a84cd839e0fe34e56d45d