Zobrazeno 1 - 10
of 44
pro vyhledávání: '"Pol Adrian"'
Autor:
Tsoi Ho Fung, Pol Adrian Alan, Loncar Vladimir, Govorkova Ekaterina, Cranmer Miles, Dasu Sridhara, Elmer Peter, Harris Philip, Ojalvo Isobel, Pierini Maurizio
Publikováno v:
EPJ Web of Conferences, Vol 295, p 09036 (2024)
The high-energy physics community is investigating the potential of deploying machine-learning-based solutions on Field-Programmable Gate Arrays (FPGAs) to enhance physics sensitivity while still meeting data processing time constraints. In this cont
Externí odkaz:
https://doaj.org/article/817d76a9edcc44f6a31f1c9c7616cb98
Autor:
Trautmann, Dietrich, Ostapuk, Natalia, Grail, Quentin, Pol, Adrian Alan, Bonifazi, Guglielmo, Gao, Shang, Gajek, Martin
In high-stakes domains like legal question-answering, the accuracy and trustworthiness of generative AI systems are of paramount importance. This work presents a comprehensive benchmark of various methods to assess the groundedness of AI-generated re
Externí odkaz:
http://arxiv.org/abs/2410.08764
Autor:
Pol Adrian Alan, Aarrestad Thea, Govorkova Katya, Halily Roi, Kopetz Tal, Klempner Anat, Loncar Vladimir, Ngadiuba Jennifer, Pierini Maurizio, Sirkin Olya, Summers Sioni
Publikováno v:
EPJ Web of Conferences, Vol 251, p 04027 (2021)
We apply object detection techniques based on Convolutional Neural Networks to jet reconstruction and identification at the CERN Large Hadron Collider. In particular, we focus on CaloJet reconstruction, representing each event as an image composed of
Externí odkaz:
https://doaj.org/article/cdaa8b5f1b46459e8e7aac20a08d5b03
Autor:
Pol, Adrian Alan, Govorkova, Ekaterina, Gronroos, Sonja, Chernyavskaya, Nadezda, Harris, Philip, Pierini, Maurizio, Ojalvo, Isobel, Elmer, Peter
Unsupervised deep learning techniques are widely used to identify anomalous behaviour. The performance of such methods is a product of the amount of training data and the model size. However, the size is often a limiting factor for the deployment on
Externí odkaz:
http://arxiv.org/abs/2310.06047
Autor:
Tsoi, Ho Fung, Pol, Adrian Alan, Loncar, Vladimir, Govorkova, Ekaterina, Cranmer, Miles, Dasu, Sridhara, Elmer, Peter, Harris, Philip, Ojalvo, Isobel, Pierini, Maurizio
Publikováno v:
EPJ Web of Conferences 295, 09036 (2024)
The high-energy physics community is investigating the potential of deploying machine-learning-based solutions on Field-Programmable Gate Arrays (FPGAs) to enhance physics sensitivity while still meeting data processing time constraints. In this cont
Externí odkaz:
http://arxiv.org/abs/2305.04099
Autor:
Zandonati, Ben, Bucagu, Glenn, Pol, Adrian Alan, Pierini, Maurizio, Sirkin, Olya, Kopetz, Tal
Model compression is instrumental in optimizing deep neural network inference on resource-constrained hardware. The prevailing methods for network compression, namely quantization and pruning, have been shown to enhance efficiency at the cost of perf
Externí odkaz:
http://arxiv.org/abs/2302.07612
Autor:
Pol Adrian Alan, Azzolini Virginia, Cerminara Gianluca, De Guio Federico, Franzoni Giovanni, Pierini Maurizio, Siroký Filip, Vlimant Jean-Roch
Publikováno v:
EPJ Web of Conferences, Vol 214, p 06008 (2019)
The certification of the CMS experiment data as usable for physics analysis is a crucial task to ensure the quality of all physics results published by the collaboration. Currently, the certification conducted by human experts is labor intensive and
Externí odkaz:
https://doaj.org/article/fd3ce145343f4480b99815c211324cf5
Autor:
Azzolin Virginia, Andrews Michael, Cerminara Gianluca, Dev Nabarun, Jessop Colin, Marinelli Nancy, Mudholkar Tanmay, Pierini Maurizio, Pol Adrian, Vlimant Jean-Roch
Publikováno v:
EPJ Web of Conferences, Vol 214, p 01007 (2019)
The Compact Muon Solenoid (CMS) experiment dedicates significant effort to assess the quality of its data, online and offline. A real-time data quality monitoring system is in place to spot and diagnose problems as promptly as possible to avoid data
Externí odkaz:
https://doaj.org/article/23af7a5d1f704551aad3458270f74ce4
Model compression is vital to the deployment of deep learning on edge devices. Low precision representations, achieved via quantization of weights and activations, can reduce inference time and memory requirements. However, quantifying and predicting
Externí odkaz:
http://arxiv.org/abs/2210.08502
Autor:
Pol, Adrian Alan, Aarrestad, Thea, Govorkova, Ekaterina, Halily, Roi, Klempner, Anat, Kopetz, Tal, Loncar, Vladimir, Ngadiuba, Jennifer, Pierini, Maurizio, Sirkin, Olya, Summers, Sioni
We apply object detection techniques based on deep convolutional blocks to end-to-end jet identification and reconstruction tasks encountered at the CERN Large Hadron Collider (LHC). Collision events produced at the LHC and represented as an image co
Externí odkaz:
http://arxiv.org/abs/2202.04499