Zobrazeno 1 - 10
of 1 231
pro vyhledávání: '"Roy Avik"'
Autor:
Li Haoyang, Duarte Javier, Roy Avik, Zhu Ruike, Huerta E. A., Diaz Daniel, Harris Philip, Kansal Raghav, Katz Daniel S., Kavoori Ishaan H., Kindratenko Volodymyr V., Mokhtar Farouk, Neubauer Mark S., Park Sang Eon, Quinnan Melissa, Rusack Roger, Zhao Zhizhen
Publikováno v:
EPJ Web of Conferences, Vol 295, p 09017 (2024)
The findable, accessible, interoperable, and reusable (FAIR) data principles serve as a framework for examining, evaluating, and improving data sharing to advance scientific endeavors. There is an emerging trend to adapt these principles for machine
Externí odkaz:
https://doaj.org/article/4ab127e3bc5b41e2a36e37e44ea0a8e1
Autor:
Duarte, Javier, Li, Haoyang, Roy, Avik, Zhu, Ruike, Huerta, E. A., Diaz, Daniel, Harris, Philip, Kansal, Raghav, Katz, Daniel S., Kavoori, Ishaan H., Kindratenko, Volodymyr V., Mokhtar, Farouk, Neubauer, Mark S., Park, Sang Eon, Quinnan, Melissa, Rusack, Roger, Zhao, Zhizhen
Publikováno v:
Mach. Learn.: Sci. Technol. 4 (2023) 045062
The findable, accessible, interoperable, and reusable (FAIR) data principles provide a framework for examining, evaluating, and improving how data is shared to facilitate scientific discovery. Generalizing these principles to research software and ot
Externí odkaz:
http://arxiv.org/abs/2212.05081
Autor:
Roy, Avik
In recent years, digital object management practices to support findability, accessibility, interoperability, and reusability (FAIR) have begun to be adopted across a number of data-intensive scientific disciplines. These digital objects include data
Externí odkaz:
http://arxiv.org/abs/2211.15021
Autor:
Roy, Avik, Neubauer, Mark S.
Multivariate techniques and machine learning models have found numerous applications in High Energy Physics (HEP) research over many years. In recent times, AI models based on deep neural networks are becoming increasingly popular for many of these a
Externí odkaz:
http://arxiv.org/abs/2211.12770
Extracting scientific results from high-energy collider data involves the comparison of data collected from the experiments with synthetic data produced from computationally-intensive simulations. Comparisons of experimental data and predictions from
Externí odkaz:
http://arxiv.org/abs/2211.11910
Recent developments in the methods of explainable AI (XAI) allow researchers to explore the inner workings of deep neural networks (DNNs), revealing crucial information about input-output relationships and realizing how data connects with machine lea
Externí odkaz:
http://arxiv.org/abs/2210.04371
Autor:
Huerta, E. A., Blaiszik, Ben, Brinson, L. Catherine, Bouchard, Kristofer E., Diaz, Daniel, Doglioni, Caterina, Duarte, Javier M., Emani, Murali, Foster, Ian, Fox, Geoffrey, Harris, Philip, Heinrich, Lukas, Jha, Shantenu, Katz, Daniel S., Kindratenko, Volodymyr, Kirkpatrick, Christine R., Lassila-Perini, Kati, Madduri, Ravi K., Neubauer, Mark S., Psomopoulos, Fotis E., Roy, Avik, Rübel, Oliver, Zhao, Zhizhen, Zhu, Ruike
Publikováno v:
Scientific Data 10, 487 (2023)
A foundational set of findable, accessible, interoperable, and reusable (FAIR) principles were proposed in 2016 as prerequisites for proper data management and stewardship, with the goal of enabling the reusability of scholarly data. The principles w
Externí odkaz:
http://arxiv.org/abs/2210.08973
Research in the data-intensive discipline of high energy physics (HEP) often relies on domain-specific digital contents. Reproducibility of research relies on proper preservation of these digital objects. This paper reflects on the interpretation of
Externí odkaz:
http://arxiv.org/abs/2209.09752
Autor:
Benelli, Gabriele, Chen, Thomas Y., Duarte, Javier, Feickert, Matthew, Graham, Matthew, Gray, Lindsey, Hackett, Dan, Harris, Phil, Hsu, Shih-Chieh, Kasieczka, Gregor, Khoda, Elham E., Komm, Matthias, Liu, Mia, Neubauer, Mark S., Norberg, Scarlet, Perloff, Alexx, Rieger, Marcel, Savard, Claire, Terao, Kazuhiro, Thais, Savannah, Roy, Avik, Vlimant, Jean-Roch, Chachamis, Grigorios
The growing role of data science (DS) and machine learning (ML) in high-energy physics (HEP) is well established and pertinent given the complex detectors, large data, sets and sophisticated analyses at the heart of HEP research. Moreover, exploiting
Externí odkaz:
http://arxiv.org/abs/2207.09060