Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Gissella Bejarano"'
Autor:
Pablo Rivas, Christopher Thompson, Brenda Tafur, Bikram Khanal, Olawale Ayoade, Tonni Das Jui, Korn Sooksatra, Javier Orduz, Gissella Bejarano
Publikováno v:
Artificial Intelligence in Earth Science ISBN: 9780323917377
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0d8d6365af216c62b3f15b52aaf8a98f
https://doi.org/10.1016/b978-0-323-91737-7.00007-4
https://doi.org/10.1016/b978-0-323-91737-7.00007-4
Autor:
Sahara Ali, Ahmed Alnuaim (Alnaim), Olawale Ayoade, Jayme Garcia Arnal Barbedo, Colin M. Beier, Gissella Bejarano, Andrew Bennett, Guido Cervone, Nicoleta Cristea, Annie Didier, Geetha Satya Mounika Ganji, Edwin Goh, Weiming Hu, Yiyi Huang, Didarul Islam, Aji John, Lucas K. Johnson, Tonni Das Jui, Amruta Kale, Bikram Khanal, Wai Hang Chow Lin, Xiaogang Ma, Michael J. Mahoney, Arif Masrur, Javier Orduz, Nurul Rafi, Pablo Rivas, Korn Sooksatra, Ziheng Sun, Brenda Tafur, Christopher Thompson, Jianwu Wang, Jinbo Wang, Kehan Yang, George Young, Manzhu Yu
Publikováno v:
Artificial Intelligence in Earth Science ISBN: 9780323917377
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::70bc35a105902d2df48ece638ee5ddaa
https://doi.org/10.1016/b978-0-323-91737-7.09991-6
https://doi.org/10.1016/b978-0-323-91737-7.09991-6
Publikováno v:
LatinX in AI at North American Chapter of the Association for Computational Linguistics Conference 2022.
The transformer-based architectures have achieved remarkable success in several Natural Language Processing tasks, such as the Question Answering domain. Our research focuses on different transformer-based language models’ performance in software d
Publikováno v:
LatinX in AI at North American Chapter of the Association for Computational Linguistics Conference 2022.
This work proposes a methodology to derive latent representations for highly noisy text. Traditionally in Natural Language Processing systems, methods rely on words as the core components of a text. Unlike those, we propose a character-based approach
Publikováno v:
AAAI
Thoroughly understanding how energy consumption is disaggregated into individual appliances can help reduce household expenses, integrate renewable sources of energy, and lead to efficient use of energy. In this work, we propose a deep latent generat
Publikováno v:
Computational Data and Social Networks ISBN: 9783030914332
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::410662b7f34e5f57e8a0b139b23e1b42
https://doi.org/10.1007/978-3-030-91434-9_18
https://doi.org/10.1007/978-3-030-91434-9_18
Publikováno v:
iThings/GreenCom/CPSCom/SmartData/Cybermatics
Accurately predicting resolution time for emergency incidents is crucial for public safety and smooth functioning of cities as it helps in planning resources that will be available for immediate assistance. In this paper, we present DeepER, a deep le
Publikováno v:
BuildSys@SenSys
Accurately predicting water consumption in residential and commercial buildings is essential for identifying possible leaks, minimizing water wastage, and for paving the way for a sustainable future. In this paper, we present SWaP, a Smart Water Pred
Publikováno v:
SMARTCOMP
Water availability and management is an important problem plaguing many developing and under-developed countries. Many factors including geographic, political, management, and environmental factors affect the availability of water in these regions. I
Autor:
Gissella Bejarano
Publikováno v:
SMARTCOMP
Energy and water related problems are becoming more relevant due to their huge impact on our environment. The limited availability of resources necessitates the development of machine learning prediction models that can help in predicting demand and