Zobrazeno 1 - 10
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pro vyhledávání: '"Acharya, Dinesh"'
Autor:
Iqbal, Faiza, Chandra, Prashant, Lewis, Leslie Edward S., Acharya, Dinesh, Purkayastha, Jayashree, Shenoy, Padmaja A., Kumar Patil, Anand
Publikováno v:
In Journal of Neonatal Nursing April 2024 30(2):141-147
Akademický článek
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Autor:
Iqbal, Faiza, Chandra, Prashant, Khan, Aakif Ashar, Edward S Lewis, Leslie, Acharya, Dinesh, Vandana, K.E., Jayashree, P., Shenoy, Padmaja A.
Publikováno v:
In Clinical Epidemiology and Global Health November-December 2023 24
Autor:
Wu, Jiqing, Huang, Zhiwu, Acharya, Dinesh, Li, Wen, Thoma, Janine, Paudel, Danda Pani, Van Gool, Luc
In generative modeling, the Wasserstein distance (WD) has emerged as a useful metric to measure the discrepancy between generated and real data distributions. Unfortunately, it is challenging to approximate the WD of high-dimensional distributions. I
Externí odkaz:
http://arxiv.org/abs/1904.05408
The extension of image generation to video generation turns out to be a very difficult task, since the temporal dimension of videos introduces an extra challenge during the generation process. Besides, due to the limitation of memory and training sta
Externí odkaz:
http://arxiv.org/abs/1810.02419
Classifying facial expressions into different categories requires capturing regional distortions of facial landmarks. We believe that second-order statistics such as covariance is better able to capture such distortions in regional facial fea- tures.
Externí odkaz:
http://arxiv.org/abs/1805.04855
In many domains of computer vision, generative adversarial networks (GANs) have achieved great success, among which the family of Wasserstein GANs (WGANs) is considered to be state-of-the-art due to the theoretical contributions and competitive quali
Externí odkaz:
http://arxiv.org/abs/1712.01026
Autor:
Chen, Wei, Li, Mian, Peng, Wen-Li, Huang, Ling, Zhao, Chao, Acharya, Dinesh, Liu, Wentao, Zheng, Anmin
Publikováno v:
In Green Energy & Environment April 2022 7(2):296-306
Autor:
Wu, Jiqing, Huang, Zhiwu, Acharya, Dinesh, Li, Wen, Thoma, Janine, Paudel, Danda Pani, Van Gool, Luc
In generative modeling, the Wasserstein distance (WD) has emerged as a useful metric to measure the discrepancy between generated and real data distributions. Unfortunately, it is challenging to approximate the WD of high-dimensional distributions. I
Externí odkaz:
http://arxiv.org/abs/1706.02631
Akademický článek
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