Zobrazeno 1 - 10
of 61
pro vyhledávání: '"Guna, R."'
Conventional scaling of neural networks typically involves designing a base network and growing different dimensions like width, depth, etc. of the same by some predefined scaling factors. We introduce an automated scaling approach leveraging second-
Externí odkaz:
http://arxiv.org/abs/2402.12418
We propose a novel method that trains a conditional Generative Adversarial Network (GAN) to generate visual interpretations of a Convolutional Neural Network (CNN). To comprehend a CNN, the GAN is trained with information on how the CNN processes an
Externí odkaz:
http://arxiv.org/abs/2301.08067
Akademický článek
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Publikováno v:
In Journal of Molecular Structure 5 January 2020 1199
Akademický článek
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Autor:
Shrestha, Min B., Bhatta, Guna R.
Publikováno v:
In The Journal of Finance and Data Science June 2018 4(2):71-89
Autor:
Min B. Shrestha, Guna R. Bhatta
Publikováno v:
Journal of Finance and Data Science, Vol 4, Iss 2, Pp 71-89 (2018)
Economists face method selection problem while working with time series data. As time series data may possess specific properties such as trend and structural break, common methods used to analyze other types of data may not be appropriate for the an
Externí odkaz:
https://doaj.org/article/ebe334ead947453ab2f65b0548559005
We propose a novel method that trains a conditional Generative Adversarial Network (GAN) to generate visual interpretations of a Convolutional Neural Network (CNN). To comprehend a CNN, the GAN is trained with information on how the CNN processes an
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::04855a4227bf4c92c8f18a724407f9f8
Autor:
Kurt S. Myers, Bishnu P. Bhattarai, Jingyuan Wang, Oguzhan Ceylan, Sumit Paudyal, Guna R. Bharati
Publikováno v:
IEEE Transactions on Industrial Informatics. 15:54-63
We develop hierarchical coordination frameworks to optimally manage active and reactive power dispatch of number of spatially distributed electric vehicles (EVs) incorporating distribution grid level constraints. The frameworks consist of detailed ma
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::06f6314193146f1091dff0dfd51ea9a4
https://doi.org/10.1109/tii.2018.2829710
https://doi.org/10.1109/tii.2018.2829710
Autor:
Pratyush Chakraborty, Guna R. Bharati, Bo Chen, Adilson E. Motter, Takashi Nishikawa, Chao Duan
We report on a real-time demand response experiment with 100 controllable devices. The experiment reveals several key challenges in the deployment of a real-time demand response program, including time delays, uncertainties, characterization errors,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::746ac11f1c06c47c29d8156898ae0541