Zobrazeno 1 - 10
of 22
pro vyhledávání: '"Shashanka Ubaru"'
Publikováno v:
Algorithms, Vol 13, Iss 11, p 295 (2020)
We propose and investigate two new methods to approximate f(A)b for large, sparse, Hermitian matrices A. Computations of this form play an important role in numerous signal processing and machine learning tasks. The main idea behind both methods is t
Externí odkaz:
https://doaj.org/article/2147c06644794545b9d1dccafd842f16
Publikováno v:
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
Publikováno v:
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
Publikováno v:
ACM SIGMETRICS Performance Evaluation Review. 50:42-44
It is well known that strong connections exist between random walks (RWs) in the nonnegative quarter plane and the mathematical performance modeling, analysis and optimization of computer systems and communication networks. Examples include adaptive
Autor:
Ismail Yunus Akhalwaya, Yang-Hui He, Lior Horesh, Vishnu Jejjala, William Kirby, Kugendran Naidoo, Shashanka Ubaru
The boundary operator is a linear operator that acts on a collection of high-dimensional binary points (simplices) and maps them to their boundaries. This boundary map is one of the key components in numerous applications, including differential equa
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b712ebac83c9b93ef727246c12f2ad40
http://arxiv.org/abs/2201.11510
http://arxiv.org/abs/2201.11510
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:
Shashanka Ubaru, Shivmaran S. Pandian, Saurabh Raje, Yogish Sabharwal, Toyotaro Suzumura, Venkatesan T. Chakaravarthy
Publikováno v:
SC
We present distributed algorithms for training dynamic Graph Neural Networks (GNN) on large scale graphs spanning multi-node, multi-GPU systems. To the best of our knowledge, this is the first scaling study on dynamic GNN. We devise mechanisms for re
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fd7c41e2b362488de70c281bc8023b46
Publikováno v:
ICASSP
In recent years, a variety of randomized constructions of sketching matrices have been devised, that have been used in fast algorithms for numerical linear algebra problems, such as least squares regression, low-rank approximation, and the approximat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::160d3ff35d8ba0b82850e5ce1f204053
Publikováno v:
Journal of Biomedical Informatics
In this study, we address three important challenges related to disease transmissions such as the COVID-19 pandemic, namely, (a) providing an early warning to likely exposed individuals, (b) identifying individuals who are asymptomatic, and (c) presc