Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Turaga, Pavan K."'
Unsupervised domain adaptation (UDA) aims to transfer and adapt knowledge from a labeled source domain to an unlabeled target domain. Traditionally, subspace-based methods form an important class of solutions to this problem. Despite their mathematic
Externí odkaz:
http://arxiv.org/abs/2201.01806
Compressive sensing (CS) is a new approach for the acquisition and recovery of sparse signals and images that enables sampling rates significantly below the classical Nyquist rate. Despite significant progress in the theory and methods of CS, little
Externí odkaz:
http://arxiv.org/abs/1201.4895
Publikováno v:
Ankita Shukla
Deep neural networks have increasingly been used as an auxiliary tool in healthcare applications, due to their ability to improve performance of several diagnosis tasks. However, these methods are not widely adopted in clinical settings due to the pr
Autor:
Srivastava, Anuj, Turaga, Pavan K.
Publikováno v:
Riemannian Computing in Computer Vision; 2016, p1-18, 18p
Akademický článek
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Publikováno v:
Computer Vision - Eccv 2010; 2010, p129-142, 14p
Autor:
Ankita Shukla, Rushil Anirudh, Eugene Kur, Thiagarajan, Jayaraman J., Peer-Timo Bremer, Spears, Brian K., Tammy Ma, Turaga, Pavan K.
Publikováno v:
Ankita Shukla
In this paper, we develop a Wasserstein autoencoder (WAE) with a hyperspherical prior for multimodal data in the application of inertial confinement fusion. Unlike a typical hyperspherical generative model that requires computationally inefficient sa
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::48715514fcb6902efb7874b925604811
https://arxiv.org/abs/2111.12798
https://arxiv.org/abs/2111.12798
Autor:
John Kevin Cava, Vant, John W., Nicholas Ho, Ankita Shukla, Turaga, Pavan K., Ross Maciejewski, Abhishek Singharoy
Publikováno v:
Ankita Shukla
In this paper, we utilized generative models, and reformulate it for problems in molecular dynamics (MD) simulation, by introducing an MD potential energy component to our generative model. By incorporating potential energy as calculated from TorchMD
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2fb1974cd56704363a896f3bd785bd8f
https://arxiv.org/abs/2111.14053
https://arxiv.org/abs/2111.14053