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pro vyhledávání: '"Chaudhari, Shravan"'
In real-world graph data, distribution shifts can manifest in various ways, such as the emergence of new categories and changes in the relative proportions of existing categories. It is often important to detect nodes of novel categories under such d
Externí odkaz:
http://arxiv.org/abs/2404.01216
Autor:
Singh, Pranav, Chukkapalli, Raviteja, Chaudhari, Shravan, Chen, Luoyao, Chen, Mei, Pan, Jinqian, Smuda, Craig, Cirrone, Jacopo
Publikováno v:
Singh, P., Chukkapalli, R., Chaudhari, S. et al. Shifting to machine supervision: annotation-efficient semi and self-supervised learning for automatic medical image segmentation and classification. Sci Rep 14, 10820 (2024)
Advancements in clinical treatment are increasingly constrained by the limitations of supervised learning techniques, which depend heavily on large volumes of annotated data. The annotation process is not only costly but also demands substantial time
Externí odkaz:
http://arxiv.org/abs/2311.10319
Deep learning techniques have been proven to provide excellent performance for a variety of high-energy physics applications, such as particle identification, event reconstruction and trigger operations. Recently, we developed an end-to-end deep lear
Externí odkaz:
http://arxiv.org/abs/2309.14254
Autor:
Singh, Pranav, Chen, Luoyao, Chen, Mei, Pan, Jinqian, Chukkapalli, Raviteja, Chaudhari, Shravan, Cirrone, Jacopo
The task of medical image segmentation presents unique challenges, necessitating both localized and holistic semantic understanding to accurately delineate areas of interest, such as critical tissues or aberrant features. This complexity is heightene
Externí odkaz:
http://arxiv.org/abs/2308.10488
Autor:
Chaudhari, Purva1, Chaudhari, Shravan1, Chudasama, Ruchi1 ruchi.chudasama@cern.ch, Gleyzer, Sergei1
Publikováno v:
EPJ Web of Conferences. 5/6/2024, Vol. 295, p1-9. 9p.
Akademický článek
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Autor:
Andrews Michael, Burkle Bjorn, Chaudhari Shravan, Di Croce Davide, Gleyzer Sergei, Heintz Ulrich, Narain Meenakshi, Paulini Manfred, Usai Emanuele
Publikováno v:
EPJ Web of Conferences, Vol 251, p 03057 (2021)
Machine learning algorithms are gaining ground in high energy physics for applications in particle and event identification, physics analysis, detector reconstruction, simulation and trigger. Currently, most data-analysis tasks at LHC experiments ben
Externí odkaz:
https://doaj.org/article/45d5fcb893f94bdaa449c946063df085
Autor:
Andrews Michael, Burkle Bjorn, Chaudhari Shravan, DiCroce Davide, Gleyzer Sergei, Heintz Ulrich, Narain Meenakshi, Paulini Manfred, Usai Emanuele
Publikováno v:
EPJ Web of Conferences, Vol 251, p 04030 (2021)
We describe a novel application of the end-to-end deep learning technique to the task of discriminating top quark-initiated jets from those originating from the hadronization of a light quark or a gluon. The end-to-end deep learning technique combine
Externí odkaz:
https://doaj.org/article/68f4b42e2190438b8f61ecf2484f33d6