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CERN Document Server 787 Datensätze gefunden  1 - 10nächsteEnde  gehen zum Datensatz: Die Suche hat 1.06 Sekunden gedauert. 
1.
Model Performance Prediction for Hyperparameter Optimization of Deep Learning Models Using High Performance Computing and Quantum Annealing / Amboage, Juan Pablo García (CERN ; U. Santiago de Compostela (main)) ; Wulff, Eric (CERN) ; Girone, Maria (CERN) ; Pena, Tomás F. (U. Santiago de Compostela (main))
Hyperparameter Optimization (HPO) of Deep Learning-based models tends to be a compute resource intensive process as it usually requires to train the target model with many different hyperparameter configurations. We show that integrating model performance prediction with early stopping methods holds great potential to speed up the HPO process of deep learning models. [...]
arXiv:2311.17508.- 2024 - 7 p. - Published in : EPJ Web Conf. 295 (2024) 12005 Fulltext: document - PDF; 2311.17508 - PDF;
In : 26th International Conference on Computing in High Energy & Nuclear Physics, Norfolk, Virginia, Us, 8 - 12 May 2023, pp.12005
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2.
Distributed hybrid quantum-classical performance prediction for hyperparameter optimization / Wulff, Eric (CERN) ; Garcia Amboage, Juan Pablo (CERN) ; Aach, Marcel (Julich, Forschungszentrum ; Iceland U.) ; Gislason, Thorsteinn Eli (Iceland U.) ; Ingolfsson, Thorsteinn Kristinn (Iceland U.) ; Ingolfsson, Tomas Kristinn (Iceland U.) ; Pasetto, Edoardo (Julich, Forschungszentrum ; RWTH Aachen U.) ; Delilbasic, Amer (Julich, Forschungszentrum ; Iceland U.) ; Riedel, Morris (Julich, Forschungszentrum ; Iceland U.) ; Sarma, Rakesh (Julich, Forschungszentrum) et al.
Hyperparameter optimization (HPO) of neural networks is a computationally expensive procedure, which requires a large number of different model configurations to be trained. To reduce such costs, this work presents a distributed, hybrid workflow, that runs the training of the neural networks on multiple graphics processing units (GPUs) on a classical supercomputer, while predicting the configurations’ performance with quantum-trained support vector regression (QT-SVR) on a quantum annealer (QA). [...]
2024 - 14 p. - Published in : Quantum Machine Intelligence 6 (2024) 59 Fulltext: PDF;
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3.
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 Introduction to the CERN Openlab Lecture Programme / Girone, Maria (speaker) (CERN)
2024 - 591. Summer Student Lecture Programme 2024; Summer Student Lecture Programme 2024 External links: Talk details; Event details In : Summer Student Lecture Programme 2024
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4.
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 Introduction to CERN openlab summer lectures programme / Girone, Maria (speaker) (CERN)
2024 - 533. CERN openlab summer student lecture programme; Welcome and introduction to CERN External links: Talk details; Event details In : Welcome and introduction to CERN
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5.
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 Introduction to CERN openlab / Girone, Maria (speaker) (CERN)
2024 - 1366. CERN openlab summer student lecture programme; Welcome and introduction to CERN External links: Talk details; Event details In : Welcome and introduction to CERN
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6.
RCS-ICT PSO: Access and Support for Heterogeneous, non-x86, and latest Single-architecture Chips / Wiebalck, Arne (CERN) ; Girone, Maria (CERN) ; Piparo, Danilo (CERN) ; Blomer, Jakob (CERN)
This PSO aims to establish a consistent framework and processes to express, follow, and resource the transition of a technology from "evaluate" to "in production", leveraging different accesses to non-x86 resources, such as industry collaborations, HPC testbeds, public cloud offerings, and on-premises services [...]
CERN-IT-Note-2024-001. - 2023.
Access to fulltext
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Welcome / Jones, Bob (speaker) (CERN) ; Girone, Maria (speaker) (CERN)
2024 - 528. Workshops and Training; 2024 CERN openlab Technical Workshop External links: Talk details; Event details In : 2024 CERN openlab Technical Workshop
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CERN openlab Phase VIII / Girone, Maria (speaker) (CERN)
2024 - 1185. Workshops and Training; 2024 CERN openlab Technical Workshop External links: Talk details; Event details In : 2024 CERN openlab Technical Workshop
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9.
Improved particle-flow event reconstruction with scalable neural networks for current and future particle detectors / Pata, Joosep (NICPB, Tallinn) ; Wulff, Eric (CERN) ; Mokhtar, Farouk (UC, San Diego) ; Southwick, David (CERN) ; Zhang, Mengke (UC, San Diego) ; Girone, Maria (CERN) ; Duarte, Javier (UC, San Diego)
Efficient and accurate algorithms are necessary to reconstruct particles in the highly granular detectors anticipated at the High-Luminosity Large Hadron Collider and the Future Circular Collider. We study scalable machine learning models for event reconstruction in electron-positron collisions based on a full detector simulation. [...]
arXiv:2309.06782.- 2024-04-10 - 21 p. - Published in : Commun. Phys. 7 (2024) 124 Fulltext: 2309.06782 - PDF; document - PDF;
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10.
Hyperparameter optimization of data-driven AI models on HPC systems / Wulff, Eric (CERN) ; Girone, Maria (CERN) ; Pata, Joosep (NICPB, Tallinn)
In the European Center of Excellence in Exascale computing "Research on AI- and Simulation-Based Engineering at Exascale" (CoE RAISE), researchers develop novel, scalable AI technologies towards Exascale. This work exercises High Performance Computing resources to perform large-scale hyperparameter optimization using distributed training on multiple compute nodes. [...]
arXiv:2203.01112.- 2023 - 6 p. - Published in : J. Phys. : Conf. Ser.: 2438 (2023) , no. 1, pp. 012092
Fulltext: document - PDF; 2203.01112 - PDF;
In : 20th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2021), Daejeon, Korea, 29 Nov - 3 Dec 2021, pp.012092
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237 GIRONE, M
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4 Girone, Mario
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