Zobrazeno 1 - 10
of 37
pro vyhledávání: '"Arun, Abhishek"'
Publikováno v:
Beni-Suef University Journal of Basic & Applied Sciences; 11/7/2023, Vol. 12 Issue 1, p1-12, 12p
Publikováno v:
Machine Translation, 2010 Jun 01. 24(2), 103-121.
Externí odkaz:
https://www.jstor.org/stable/40926418
Publikováno v:
2020 IEEE Texas Power and Energy Conference (TPEC).
The sensor measurements in power distribution systems can be potentially anomalous due to sensor malfunctions, communication failure, and cyberattacks. In this paper, a data-driven framework for network-wide anomaly detection in electric power distri
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3083cade7cafe54abcf2b1c4e6b6bdcd
https://doi.org/10.1109/tpec48276.2020.9042509
https://doi.org/10.1109/tpec48276.2020.9042509
Autor:
Imayakumar, Arun Abhishek
Sensor measurements of distribution system are uncertain due to sensor malfunctions, communication failure and cyber attacks. This thesis aims to perform anomaly detection on measurements utilizing data-driven approaches. The measurements considered
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::62a59847728ab7782f1f2890941a020e
Autor:
Arun, Abhishek
Recent advances in statistical machine translation (SMT) have used dynamic programming (DP) based beam search methods for approximate inference within probabilistic translation models. Despite their success, these methods compromise the probabilistic
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______463::43f726d942d9fe11d3b223fd015b07a0
http://hdl.handle.net/1842/4815
http://hdl.handle.net/1842/4815
Publikováno v:
Haddow, B, Arun, A & Koehn, P 2011, SampleRank Training for Phrase-based Machine Translation . in Proceedings of the Sixth Workshop on Statistical Machine Translation . Stroudsburg, PA, USA, pp. 261-271 . < http://dl.acm.org/citation.cfm?id=2132960.2132995 >
Statistical machine translation systems are normally optimised for a chosen gain function (metric) by using MERT to find the best model weights. This algorithm suffers from stability problems and cannot scale beyond 20-30 features. We present an alte
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______3094::3e03e808bc7548bb1eb02fd4f5ef82dd
https://hdl.handle.net/20.500.11820/2e87d89e-e930-4e03-8114-549823c5d968
https://hdl.handle.net/20.500.11820/2e87d89e-e930-4e03-8114-549823c5d968
Publikováno v:
Arun, A, Haddow, B & Koehn, P 2010, A Unified Approach to Minimum Risk Training and Decoding . in Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR . Stroudsburg, PA, USA, pp. 365-374 . < http://dl.acm.org/citation.cfm?id=1868850.1868906 >
We present a unified approach to performing minimum risk training and minimum Bayes risk (MBR) decoding with BLEU in a phrase-based model. Key to our approach is the use of a Gibbs sampler that allows us to explore the entire probability distribution
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______3094::119f392068804ed94e62164673090520
https://hdl.handle.net/20.500.11820/d644be21-fad1-4e33-8aec-0b6fe3835311
https://hdl.handle.net/20.500.11820/d644be21-fad1-4e33-8aec-0b6fe3835311
Conference
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Autor:
Alwis, Inosha1 (AUTHOR) inosha.alwis@med.pdn.ac.lk, Rajapaksha, Buwanaka2 (AUTHOR), Jayasanka, Chanuka2 (AUTHOR), Dharmaratne, Samath D.1 (AUTHOR)
Publikováno v:
BMC Primary Care. 6/6/2024, Vol. 25 Issue 1, p1-14. 14p.