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pro vyhledávání: '"Agarwal, Tushar"'
Autor:
Agarwal, Tushar
Machine Learning (ML) using Artificial Neural Networks (ANNs), referred to asDeep Learning (DL), is a very popular and powerful method of statisticalinference. A primary advantage of deep-learning has been the automatic learningof informative feature
Generative models learned from training using deep learning methods can be used as priors in inverse under-determined inverse problems, including imaging from sparse set of measurements. In this paper, we present a novel hierarchical deep-generative
Externí odkaz:
http://arxiv.org/abs/2212.00069
Autor:
Agarwal, Tushar, Ertin, Emre
We present CardiacGen, a Deep Learning framework for generating synthetic but physiologically plausible cardiac signals like ECG. Based on the physiology of cardiovascular system function, we propose a modular hierarchical generative model and impose
Externí odkaz:
http://arxiv.org/abs/2211.08385
Autor:
Sharma, Namrata, Venugopal, Renu, Mohanty, Sujata, Priyadarshini, K., Nagpal, Ritu, Singhal, Deepali, Bari, Aafreen, Dada, Tanuj, Maharana, Prafulla Kumar, Agarwal, Tushar, Upadhyay, Ashish Dutt
Publikováno v:
In The Ocular Surface October 2024 34:504-509
Autor:
Sharma, Namrata, Kumar, Vishal, Bari, Aafreen, Venugopal, Renu, Sharma, Shivam, Agarwal, Tushar, Dada, Tanuj, Pushker, Neelam
Publikováno v:
In The Ocular Surface October 2024 34:277-282
Autor:
Agarwal, Tushar1 (AUTHOR), Atray, Neeraj1 (AUTHOR), Sharma, Jai Gopal2 (AUTHOR) sharmajaigopal@dce.ac.in
Publikováno v:
Future Journal of Pharmaceutical Sciences. 6/18/2024, Vol. 10 Issue 1, p1-24. 24p.
We introduce PathQuery, a graph query language developed to scale with Google's query and data volumes as well as its internal developer community. PathQuery supports flexible and declarative semantics. We have found that this enables query developer
Externí odkaz:
http://arxiv.org/abs/2106.09799
Automatic Target Recognition (ATR) algorithms classify a given Synthetic Aperture Radar (SAR) image into one of the known target classes using a set of training images available for each class. Recently, learning methods have shown to achieve state-o
Externí odkaz:
http://arxiv.org/abs/2012.09284
Autor:
Agarwal, Tushar1 (AUTHOR), Manandhar, Suman1 (AUTHOR), B, Harish Kumar1 (AUTHOR), Famurewa, Ademola C.1,2 (AUTHOR), Gurram, Prasada Chowdari1 (AUTHOR), Suggala, Ramya Shri1 (AUTHOR), Sankhe, Runali1 (AUTHOR), Mudgal, Jayesh1 (AUTHOR), Pai, K. Sreedhara Ranganath1 (AUTHOR) ksr.pai@manipal.edu
Publikováno v:
Scientific Reports. 4/30/2024, Vol. 14 Issue 1, p1-15. 15p.
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