Zobrazeno 1 - 10
of 74
pro vyhledávání: '"Misha E. Kilmer"'
Publikováno v:
Frontiers in Big Data, Vol 7 (2024)
Learning from complex, multidimensional data has become central to computational mathematics, and among the most successful high-dimensional function approximators are deep neural networks (DNNs). Training DNNs is posed as an optimization problem to
Externí odkaz:
https://doaj.org/article/14b061e7a7024a4c86b5fe0849726e16
Autor:
Elizabeth Newman, Misha E. Kilmer
Publikováno v:
SIAM Journal on Imaging Sciences. 13:1084-1112
In recent work [S. Soltani, M. Kilmer, and P. C. Hansen, BIT, 56 (2016)], an algorithm for nonnegative tensor patch dictionary learning in the context of X-ray CT imaging and based on a tensor-tens...
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a0061b83822547131b752cc3136a44c5
https://doi.org/10.1137/19m1297026
https://doi.org/10.1137/19m1297026
Publikováno v:
Proc Natl Acad Sci U S A
With the advent of machine learning and its overarching pervasiveness it is imperative to devise ways to represent large datasets efficiently while distilling intrinsic features necessary for subsequent analysis. The primary workhorse used in data di
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::798fc8d4654a0b5831ee01ba16bce1ec
https://pubmed.ncbi.nlm.nih.gov/34234014
https://pubmed.ncbi.nlm.nih.gov/34234014
Publikováno v:
SIAM Journal on Scientific Computing. 41:B229-B249
In partial differential equation-based (PDE-based) inverse problems with many measurements, many large-scale discretized PDEs must be solved for each evaluation of the misfit or objective function....
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::36b4960fe2561c5c031da99923f0985d
https://doi.org/10.1137/17m1143319
https://doi.org/10.1137/17m1143319
Publikováno v:
Proceedings of the 2021 SIAM International Conference on Data Mining (SDM) ISBN: 9781611976700
Many irregular domains such as social networks, financial transactions, neuron connections, and natural language constructs are represented using graph structures. In recent years, a variety of graph neural networks (GNNs) have been successfully appl
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::77b7d81467e24d9a2e0934272b1721fe
https://doi.org/10.1137/1.9781611976700.82
https://doi.org/10.1137/1.9781611976700.82
Publikováno v:
SDM
Many irregular domains such as social networks, financial transactions, neuron connections, and natural language constructs are represented using graph structures. In recent years, a variety of graph neural networks (GNNs) have been successfully appl
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1e3861f0d544e8fc4e1d8b15f257a48d
https://doi.org/10.1137/1.9781611976700
https://doi.org/10.1137/1.9781611976700
Autor:
Misha E. Kilmer, Arvind K. Saibaba
We provide a computational framework for approximating a class of structured matrices; here, the term structure is very general, and may refer to a regular sparsity pattern (e.g., block-banded), or be more highly structured (e.g., symmetric block Toe
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::06f31c4726746aec806079a5d0e02830
This survey concerns subspace recycling methods, a popular class of iterative methods that enable effective reuse of subspace information in order to speed up convergence and find good initial guesses over a sequence of linear systems with slowly cha
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8f82da2c11fc7938196b11af1dd838aa
http://arxiv.org/abs/2001.10347
http://arxiv.org/abs/2001.10347
We present a new inner–outer iterative algorithm for edge enhancement in imaging problems. At each outer iteration, we formulate a Tikhonov-regularized problem where the penalization is expressed in the two-norm and involves a regularization operat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0c85965b0cab4b6eec0ceb79c9f98c11
http://arxiv.org/abs/1912.13103
http://arxiv.org/abs/1912.13103
Publikováno v:
Journal of Computational Physics. 440:110391
Markov chain Monte Carlo (MCMC) approaches are traditionally used for uncertainty quantification in inverse problems where the physics of the underlying sensor modality is described by a partial differential equation (PDE). However, the use of MCMC a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e51db3fd377d2a9dadb009d9de4541f2
https://doi.org/10.1016/j.jcp.2021.110391
https://doi.org/10.1016/j.jcp.2021.110391