Zobrazeno 1 - 10
of 242
pro vyhledávání: '"Baba C. Vemuri"'
Publikováno v:
Journal of the American Statistical Association. :1-14
The James-Stein estimator is an estimator of the multivariate normal mean and dominates the maximum likelihood estimator (MLE) under squared error loss. The original work inspired great interest in developing shrinkage estimators for a variety of pro
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d152c623cb7ecb01e094163a4da4f4d9
https://doi.org/10.1080/01621459.2022.2110877
https://doi.org/10.1080/01621459.2022.2110877
Publikováno v:
IEEE Transactions on Pattern Analysis and Machine Intelligence. 44:799-810
Geometric deep learning is a relatively nascent field that has attracted significant attention in the past few years. This is partly due to the availability of data acquired from non-euclidean domains or features extracted from euclidean-space data t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::24784a4e5f8ca85b0c6ea1be63aa01d3
https://doi.org/10.1109/tpami.2020.3003846
https://doi.org/10.1109/tpami.2020.3003846
Publikováno v:
IEEE Transactions on Pattern Analysis and Machine Intelligence. 43:3904-3917
Principal component analysis (PCA) and Kernel principal component analysis (KPCA) are fundamental methods in machine learning for dimensionality reduction. The former is a technique for finding this approximation in finite dimensions and the latter i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f79f97ed4568ed80fada067f4bef674d
https://doi.org/10.1109/tpami.2020.2992392
https://doi.org/10.1109/tpami.2020.2992392
Publikováno v:
PLoS ONE, Vol 11, Iss 6, p e0155764 (2016)
Parkinson's disease (PD) is a common and debilitating neurodegenerative disorder that affects patients in all countries and of all nationalities. Magnetic resonance imaging (MRI) is currently one of the most widely used diagnostic imaging techniques
Externí odkaz:
https://doaj.org/article/76f2f1f634e04dabaf790a75ab305c61
Autor:
David D. Fuller, Jiaqi Sun, Baba C. Vemuri, Monami Banerjee, Sara M.F. Turner, Zhixin Pan, John R. Forder, Alireza Entezari
Publikováno v:
Medical Image Analysis. 57:89-105
Diffusion-weighted magnetic resonance imaging (dMRI) is a non-invasive technique to probe the complex micro-architecture of the tissue being imaged. The diffusional properties of the tissue at the imaged resolution are well captured by the ensemble a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d6ae983778297b1d72b6051858710366
https://doi.org/10.1016/j.media.2019.06.012
https://doi.org/10.1016/j.media.2019.06.012
Convolutional neural networks have been highly successful in image-based learning tasks due to their translation equivariance property. Recent work has generalized the traditional convolutional layer of a convolutional neural network to non-Euclidean
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c9d1d2f4ff116cc5b2027b67bd651538
http://arxiv.org/abs/2106.15301
http://arxiv.org/abs/2106.15301
Autor:
Baba C. Vemuri, Chun-Hao Yang
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783030781903
IPMI
IPMI
Grassmann manifolds have been widely used to represent the geometry of feature spaces in a variety of problems in medical imaging and computer vision including but not limited to shape analysis, action recognition, subspace clustering and motion segm
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ece914b8c15d55f490f4e56da964a994
https://doi.org/10.1007/978-3-030-78191-0_11
https://doi.org/10.1007/978-3-030-78191-0_11
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783030781903
IPMI
IPMI
In this paper, we present a novel generalization of the Volterra Series, which can be viewed as a higher-order convolution, to manifold-valued functions. A special case of the manifold-valued Volterra Series (MVVS) gives us a natural extension of the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4f199a807f230ba83ca45864330d3d52
https://doi.org/10.1007/978-3-030-78191-0_24
https://doi.org/10.1007/978-3-030-78191-0_24
Autor:
Martin Bauer, Rudrasis Chakraborty, Benjamin Charlier, Nicolas Charon, Hyo-young Choi, James Damon, Loic Devilliers, Aasa Feragen, Tom Fletcher, Joan Glaunès, Polina Golland, Pietro Gori, Junpyo Hong, Sarang Joshi, Sungkyu Jung, Zhiyuan Liu, Marco Lorenzi, J.S. Marron, Stephen Marsland, Nina Miolane, Jan Modersitzki, Klas Modin, Marc Niethammer, Tom Nye, Beatriz Paniagua, Xavier Pennec, Stephen M. Pizer, Thomas Polzin, Laurent Risser, Pierre Roussillon, Jörn Schulz, Ankur Sharma, Stefan Sommer, Anuj Srivastava, Liyun Tu, Baba C. Vemuri, François-Xavier Vialard, Jared Vicory, Jiyao Wang, William M. Wells, Miaomiao Zhang, Ruiyi Zhang
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::350cb20e460a6fc027889a9e7cafa455
https://doi.org/10.1016/b978-0-12-814725-2.00005-4
https://doi.org/10.1016/b978-0-12-814725-2.00005-4
Autor:
Baba C. Vemuri, Rudrasis Chakraborty
Finding the Riemannian barycenter (center of mass) or the Frechet mean (FM) of manifold-valued data sets is a commonly encountered problem in a variety of fields of science and engineering, including, but not limited to, medical image computing, mach
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::042beb6f69fe31b9f0b16cf675b17db2
https://doi.org/10.1016/b978-0-12-814725-2.00015-7
https://doi.org/10.1016/b978-0-12-814725-2.00015-7