Zobrazeno 1 - 10
of 36
pro vyhledávání: '"Daniëls, Noah"'
Autor:
Daniëls, Noah, Geerts, Floris
Graph Neural Networks (GNNs) have become an essential tool for analyzing graph-structured data, leveraging their ability to capture complex relational information. While the expressivity of GNNs, particularly their equivalence to the Weisfeiler-Leman
Externí odkaz:
http://arxiv.org/abs/2410.07829
The Big Data explosion has necessitated the development of search algorithms that scale sub-linearly in time and memory. While compression algorithms and search algorithms do exist independently, few algorithms offer both, and those which do are doma
Externí odkaz:
http://arxiv.org/abs/2409.12161
Autor:
Prior, Morgan E., Howard III, Thomas J., McLaughlin, Oliver, Ferguson, Terrence, Ishaq, Najib, Daniels, Noah M.
The ongoing Big Data explosion has created a demand for efficient and scalable algorithms for similarity search. Most recent work has focused on \textit{approximate} $k$-NN search, and while this may be sufficient for some applications, \textit{exact
Externí odkaz:
http://arxiv.org/abs/2309.05491
Autor:
Shpilker, Polina, Freeman, John, McKelvie, Hailey, Ashey, Jill, Fonticella, Jay-Miguel, Putnam, Hollie, Greenberg, Jane, Cowen, Lenore J., Couch, Alva, Daniels, Noah M.
Reproducibility of research is essential for science. However, in the way modern computational biology research is done, it is easy to lose track of small, but extremely critical, details. Key details, such as the specific version of a software used
Externí odkaz:
http://arxiv.org/abs/2204.09610
Anomaly and outlier detection is a long-standing problem in machine learning. In some cases, anomaly detection is easy, such as when data are drawn from well-characterized distributions such as the Gaussian. However, when data occupy high-dimensional
Externí odkaz:
http://arxiv.org/abs/2103.11774
Akademický článek
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Both astronomy and biology are experiencing explosive growth of data, resulting in a "big data" problem that stands in the way of a "big data" opportunity for discovery. One common question asked of such data is that of approximate search ($\rho-$nea
Externí odkaz:
http://arxiv.org/abs/1908.08551
Autor:
Durmaz, Arda, Gurnari, Carmelo, Hershberger, Courtney E., Pagliuca, Simona, Daniels, Noah, Awada, Hassan, Awada, Hussein, Adema, Vera, Mori, Minako, Ponvilawan, Ben, Kubota, Yasuo, Kewan, Tariq, Bahaj, Waled S., Barnard, John, Scott, Jacob, Padgett, Richard A., Haferlach, Torsten, Maciejewski, Jaroslaw P., Visconte, Valeria
Publikováno v:
In iScience 17 March 2023 26(3)
Autor:
Visconti, Lauren M., Cotter, Joshua A., Schick, Evan E., Daniels, Noah, Viray, Frederick E., Purcell, Carson A., Brotman, Cate B.R., Ruhman, Karen E., Escobar, Kurt A.
Publikováno v:
In Metabolism Open December 2021 12
Publikováno v:
Cell Systems, Volume 1, Issue 2, 130-140, 2015
Many datasets exhibit a well-defined structure that can be exploited to design faster search tools, but it is not always clear when such acceleration is possible. Here, we introduce a framework for similarity search based on characterizing a dataset'
Externí odkaz:
http://arxiv.org/abs/1503.05638