Zobrazeno 1 - 10
of 123
pro vyhledávání: '"Deanna Needell"'
Autor:
Lara Kassab, Alona Kryshchenko, Hanbaek Lyu, Denali Molitor, Deanna Needell, Elizaveta Rebrova, Jiahong Yuan
Publikováno v:
Frontiers in Applied Mathematics and Statistics, Vol 10 (2024)
Temporal text data, such as news articles or Twitter feeds, often comprises a mixture of long-lasting trends and transient topics. Effective topic modeling strategies should detect both types and clearly locate them in time. We first demonstrate that
Externí odkaz:
https://doaj.org/article/ee6b5308efdf457198bbdd709e663400
Publikováno v:
Frontiers in Applied Mathematics and Statistics, Vol 10 (2024)
The learning speed of feed-forward neural networks is notoriously slow and has presented a bottleneck in deep learning applications for several decades. For instance, gradient-based learning algorithms, which are used extensively to train neural netw
Externí odkaz:
https://doaj.org/article/93aa007b2d714c50bbb289b0bfe02f4c
Publikováno v:
Algorithms, Vol 16, Iss 4, p 187 (2023)
Nonnegative matrix factorization can be used to automatically detect topics within a corpus in an unsupervised fashion. The technique amounts to an approximation of a nonnegative matrix as the product of two nonnegative matrices of lower rank. In cer
Externí odkaz:
https://doaj.org/article/7db30d8638884f6ab7a332b4dfedd5f6
Autor:
Pengyu Li, Christine Tseng, Yaxuan Zheng, Joyce A. Chew, Longxiu Huang, Benjamin Jarman, Deanna Needell
Publikováno v:
Algorithms, Vol 15, Iss 5, p 136 (2022)
Classification and topic modeling are popular techniques in machine learning that extract information from large-scale datasets. By incorporating a priori information such as labels or important features, methods have been developed to perform classi
Externí odkaz:
https://doaj.org/article/b8c679f2d8e64ff0bc0e402d837cf110
Publikováno v:
Journal of Imaging, Vol 7, Iss 7, p 110 (2021)
Matrix completion, the problem of completing missing entries in a data matrix with low-dimensional structure (such as rank), has seen many fruitful approaches and analyses. Tensor completion is the tensor analog that attempts to impute missing tensor
Externí odkaz:
https://doaj.org/article/6d6300d62677441c99d4eb9847f8bf68
Publikováno v:
Algorithms, Vol 13, Iss 12, p 334 (2020)
Lyme disease is a rapidly growing illness that remains poorly understood within the medical community. Critical questions about when and why patients respond to treatment or stay ill, what kinds of treatments are effective, and even how to properly d
Externí odkaz:
https://doaj.org/article/bc57870233454bc08d3a5a802c4a762a
Autor:
Lorraine Johnson, Mira Shapiro, Raphael B. Stricker, Joshua Vendrow, Jamie Haddock, Deanna Needell
Publikováno v:
Healthcare, Vol 8, Iss 4, p 383 (2020)
There is considerable uncertainty regarding treatment of Lyme disease patients who do not respond fully to initial short-term antibiotic therapy. Choosing the best treatment approach and duration remains challenging because treatment response among t
Externí odkaz:
https://doaj.org/article/8db037de8c7b472592aee43037b94ad8
Publikováno v:
Applied and Computational Harmonic Analysis. 66:161-192
Publikováno v:
SIAM Journal on Matrix Analysis and Applications. 43:605-637
Often in applications ranging from medical imaging and sensor networks to error correction and data science (and beyond), one needs to solve large-scale linear systems in which a fraction of the measurements have been corrupted. We consider solving s
Linear regression is effective at identifying interpretable trends in a data set, but averages out potentially different effects on subgroups within data. We propose an iterative algorithm based on the randomized Kaczmarz (RK) method to automatically
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::60861badece5f1a7192c27965d1c532a
http://arxiv.org/abs/2212.03962
http://arxiv.org/abs/2212.03962