Zobrazeno 1 - 10
of 25
pro vyhledávání: '"Gyawali, Prashnna Kumar"'
Autor:
Lousto, Carlos O., Missel, Ryan, Prajapati, Harsh, Fiscella, Valentina Sosa, Armengol, Federico G. López, Gyawali, Prashnna Kumar, Wang, Linwei, Cahill, Nathan, Combi, Luciano, del Palacio, Santiago, Combi, Jorge A., Gancio, Guillermo, García, Federico, Gutiérrez, Eduardo M., Hauscarriaga, Fernando
We study individual pulses of Vela (PSR\ B0833-45\,/\,J0835-4510) from daily observations of over three hours (around 120,000 pulses per observation), performed simultaneously with the two radio telescopes at the Argentine Institute of Radioastronomy
Externí odkaz:
http://arxiv.org/abs/2108.13462
The success of deep learning relies on the availability of large-scale annotated data sets, the acquisition of which can be costly, requiring expert domain knowledge. Semi-supervised learning (SSL) mitigates this challenge by exploiting the behavior
Externí odkaz:
http://arxiv.org/abs/2009.11416
Autor:
Jiang, Xiajun, Ghimire, Sandesh, Dhamala, Jwala, Li, Zhiyuan, Gyawali, Prashnna Kumar, Wang, Linwei
Publikováno v:
International Conference on Medical Image Computing and Computer-Assisted Intervention, vol. 12266, 2020, pp. 487-496
Deep neural networks have shown great potential in image reconstruction problems in Euclidean space. However, many reconstruction problems involve imaging physics that are dependent on the underlying non-Euclidean geometry. In this paper, we present
Externí odkaz:
http://arxiv.org/abs/2007.09522
Computer-aided diagnosis via deep learning relies on large-scale annotated data sets, which can be costly when involving expert knowledge. Semi-supervised learning (SSL) mitigates this challenge by leveraging unlabeled data. One effective SSL approac
Externí odkaz:
http://arxiv.org/abs/2005.11217
Learning rich representation from data is an important task for deep generative models such as variational auto-encoder (VAE). However, by extracting high-level abstractions in the bottom-up inference process, the goal of preserving all factors of va
Externí odkaz:
http://arxiv.org/abs/2002.10549
Autor:
Gyawali, Prashnna Kumar, Li, Zhiyuan, Knight, Cameron, Ghimire, Sandesh, Horacek, B. Milan, Sapp, John, Wang, Linwei
To improve the ability of VAE to disentangle in the latent space, existing works mostly focus on enforcing independence among the learned latent factors. However, the ability of these models to disentangle often decreases as the complexity of the gen
Externí odkaz:
http://arxiv.org/abs/1909.01839
The success of deep learning in medical imaging is mostly achieved at the cost of a large labeled data set. Semi-supervised learning (SSL) provides a promising solution by leveraging the structure of unlabeled data to improve learning from a small se
Externí odkaz:
http://arxiv.org/abs/1907.09607
Autor:
Ghimire, Sandesh, Dhamala, Jwala, Gyawali, Prashnna Kumar, Sapp, John L, Horacek, B. Milan, Wang, Linwei
Publikováno v:
In International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 508-516. Springer, Cham, 2018
Noninvasive reconstruction of cardiac transmembrane potential (TMP) from surface electrocardiograms (ECG) involves an ill-posed inverse problem. Model-constrained regularization is powerful for incorporating rich physiological knowledge about spatiot
Externí odkaz:
http://arxiv.org/abs/1905.04803
Autor:
Ghimire, Sandesh, Gyawali, Prashnna Kumar, Dhamala, Jwala, Sapp, John L, Horacek, Milan, Wang, Linwei
Publikováno v:
International Conference on Information Processing and Medical Imaging 2019
Deep learning networks have shown state-of-the-art performance in many image reconstruction problems. However, it is not well understood what properties of representation and learning may improve the generalization ability of the network. In this pap
Externí odkaz:
http://arxiv.org/abs/1903.02948
Deep learning models have shown state-of-the-art performance in many inverse reconstruction problems. However, it is not well understood what properties of the latent representation may improve the generalization ability of the network. Furthermore,
Externí odkaz:
http://arxiv.org/abs/1810.05713