Zobrazeno 1 - 10
of 28
pro vyhledávání: '"Dhavala, Soma S."'
Mining large datasets and obtaining calibrated predictions from tem is of immediate relevance and utility in reliable deep learning. In our work, we develop methods for Deep neural networks based inferences in such datasets like the Gene Expression.
Externí odkaz:
http://arxiv.org/abs/2206.09333
It has long been recognized that academic success is a result of both cognitive and non-cognitive dimensions acting together. Consequently, any intelligent learning platform designed to improve learning outcomes (LOs) must provide actionable inputs t
Externí odkaz:
http://arxiv.org/abs/2010.02629
Autor:
Saha, Snehanshu, Prashanth, Tejas, Aralihalli, Suraj, Basarkod, Sumedh, Sudarshan, T. S. B, Dhavala, Soma S
We propose a theoretical framework for an adaptive learning rate policy for the Mean Absolute Error loss function and Quantile loss function and evaluate its effectiveness for regression tasks. The framework is based on the theory of Lipschitz contin
Externí odkaz:
http://arxiv.org/abs/2006.13307
Autor:
Mohapatra, Rohan, Saha, Snehanshu, Coello, Carlos A. Coello, Bhattacharya, Anwesh, Dhavala, Soma S., Saha, Sriparna
Publikováno v:
IEEE Transactions on Emerging Topics in Computational Intelligence 2021
This paper introduces AdaSwarm, a novel gradient-free optimizer which has similar or even better performance than the Adam optimizer adopted in neural networks. In order to support our proposed AdaSwarm, a novel Exponentially weighted Momentum Partic
Externí odkaz:
http://arxiv.org/abs/2006.09875
Machine Learning and Artificial Intelligence are considered an integral part of the Fourth Industrial Revolution. Their impact, and far-reaching consequences, while acknowledged, are yet to be comprehended. These technologies are very specialized, an
Externí odkaz:
http://arxiv.org/abs/2001.00818
Autor:
Konomi, Bledar A., Dhavala, Soma S., Huang, Jianhua Z., Kundu, Subrata, Huitink, David, Liang, Hong, Ding, Yu, Mallick, Bani K.
Publikováno v:
Annals of Applied Statistics 2013, Vol. 7, No. 2, 640-668
The properties of materials synthesized with nanoparticles (NPs) are highly correlated to the sizes and shapes of the nanoparticles. The transmission electron microscopy (TEM) imaging technique can be used to measure the morphological characteristics
Externí odkaz:
http://arxiv.org/abs/1312.1560
Autor:
Chakraborty, Avishek, Bingham, Derek, Dhavala, Soma S., Kuranz, Carolyn C., Drake, R. Paul, Grosskopf, Michael J., Rutter, Erica M., Torralva, Ben R., Holloway, James P., McClarren, Ryan G., Mallick, Bani K.
Publikováno v:
Technometrics, 2017 May 01. 59(2), 153-164.
Externí odkaz:
https://www.jstor.org/stable/44868915
Akademický článek
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Autor:
Dhavala, Soma S., Datta, Sujay, Mallick, Bani K., Carroll, Raymond J., Khare, Sangeeta, Lawhon, Sara D., Adams, L. Garry
Publikováno v:
Journal of the American Statistical Association, 2010 Sep 01. 105(491), 956-967.
Externí odkaz:
https://www.jstor.org/stable/27920119