Zobrazeno 1 - 10
of 12
pro vyhledávání: '"Shah Rukh"'
Visual exploration of multi-classification models with large number of classes would help machine learning experts in identifying the root cause of a problem that occurs during learning phase such as miss-classification of instances. Most of the prev
Externí odkaz:
http://arxiv.org/abs/2309.05676
The recent advancements in machine learning have motivated researchers to generate classification models dealing with hundreds of classes such as in the case of image datasets. However, visualization of classification models with high number of class
Externí odkaz:
http://arxiv.org/abs/2309.05672
Autor:
Qasim, Shah Rukh, Chernyavskaya, Nadezda, Kieseler, Jan, Long, Kenneth, Viazlo, Oleksandr, Pierini, Maurizio, Nawaz, Raheel
Publikováno v:
Eur. Phys. J. C 82, 753 (2022)
We present an end-to-end reconstruction algorithm to build particle candidates from detector hits in next-generation granular calorimeters similar to that foreseen for the high-luminosity upgrade of the CMS detector. The algorithm exploits a distance
Externí odkaz:
http://arxiv.org/abs/2204.01681
Autor:
Iiyama, Yutaro, Cerminara, Gianluca, Gupta, Abhijay, Kieseler, Jan, Loncar, Vladimir, Pierini, Maurizio, Qasim, Shah Rukh, Rieger, Marcel, Summers, Sioni, Van Onsem, Gerrit, Wozniak, Kinga, Ngadiuba, Jennifer, Di Guglielmo, Giuseppe, Duarte, Javier, Harris, Philip, Rankin, Dylan, Jindariani, Sergo, Liu, Mia, Pedro, Kevin, Tran, Nhan, Kreinar, Edward, Wu, Zhenbin
Publikováno v:
Frontiers in Big Data 3 (2021) 44
Graph neural networks have been shown to achieve excellent performance for several crucial tasks in particle physics, such as charged particle tracking, jet tagging, and clustering. An important domain for the application of these networks is the FGP
Externí odkaz:
http://arxiv.org/abs/2008.03601
Document structure analysis, such as zone segmentation and table recognition, is a complex problem in document processing and is an active area of research. The recent success of deep learning in solving various computer vision and machine learning p
Externí odkaz:
http://arxiv.org/abs/1905.13391
Publikováno v:
Eur. Phys. J. C, 79 7 (2019) 608
We explore the use of graph networks to deal with irregular-geometry detectors in the context of particle reconstruction. Thanks to their representation-learning capabilities, graph networks can exploit the full detector granularity, while natively m
Externí odkaz:
http://arxiv.org/abs/1902.07987
Autor:
Cashman, Dylan, Humayoun, Shah Rukh, Heimerl, Florian, Park, Kendall, Das, Subhajit, Thompson, John, Saket, Bahador, Mosca, Abigail, Stasko, John, Endert, Alex, Gleicher, Michael, Chang, Remco
Publikováno v:
Computer Graphics Forum 38(3) 2019, The Eurographics Association and John Wiley & Sons Ltd
Many visual analytics systems allow users to interact with machine learning models towards the goals of data exploration and insight generation on a given dataset. However, in some situations, insights may be less important than the production of an
Externí odkaz:
http://arxiv.org/abs/1809.10782
Autor:
Shah Rukh Qasim, Nadezda Chernyavskaya, Jan Kieseler, Kenneth Long, Oleksandr Viazlo, Maurizio Pierini, Raheel Nawaz
Publikováno v:
European Physical Journal
We present an end-to-end reconstruction algorithm to build particle candidates from detector hits in next-generation granular calorimeters similar to that foreseen for the high-luminosity upgrade of the CMS detector. The algorithm exploits a distance
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5ca06924cc1a438d23eb8287bf1ed429
http://arxiv.org/abs/2204.01681
http://arxiv.org/abs/2204.01681
Publikováno v:
ICDAR
Document structure analysis, such as zone segmentation and table recognition, is a complex problem in document processing and is an active area of research. The recent success of deep learning in solving various computer vision and machine learning p
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2c807179ff9a995805069133d3b1299b
http://arxiv.org/abs/1905.13391
http://arxiv.org/abs/1905.13391
Autor:
Abigail Mosca, Kendall Park, Subhajit Das, Shah Rukh Humayoun, John Thompson, Florian Heimerl, Michael Gleicher, John Stasko, Bahador Saket, Remco Chang, Alex Endert, Dylan Cashman
Many visual analytics systems allow users to interact with machine learning models towards the goals of data exploration and insight generation on a given dataset. However, in some situations, insights may be less important than the production of an
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e7029c367c16b97a8890600c7a036724