Zobrazeno 1 - 10
of 97
pro vyhledávání: '"Randal Burns"'
Autor:
Joshua T. Vogelstein, Eric W. Bridgeford, Minh Tang, Da Zheng, Christopher Douville, Randal Burns, Mauro Maggioni
Publikováno v:
Nature Communications, Vol 12, Iss 1, Pp 1-9 (2021)
Biomedical measurements usually generate high-dimensional data where individual samples are classified in several categories. Vogelstein et al. propose a supervised dimensionality reduction method which estimates the low-dimensional data projection f
Externí odkaz:
https://doaj.org/article/4e96486444d04876a7a760151e1835c7
Publikováno v:
2023 Proceedings of the Symposium on Algorithm Engineering and Experiments (ALENEX) ISBN: 9781611977561
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::24e0c5b4ace010fbf4d9fcc6715ca00c
https://doi.org/10.1137/1.9781611977561.ch13
https://doi.org/10.1137/1.9781611977561.ch13
Autor:
Justus M. Kebschull, Adam S. Charles, Talmo D. Pereira, William Silversmith, Daniel J. Tward, Joshua T. Vogelstein, Benjamin D. Pedigo, Jaewon Chung, Benjamin Falk, Satrajit S. Ghosh, Nicholas L. Turner, Randal Burns
Publikováno v:
Annu Rev Neurosci
As acquiring bigger data becomes easier in experimental brain science, computational and statistical brain science must achieve similar advances to fully capitalize on these data. Tackling these problems will benefit from a more explicit and concerte
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e17f0ab78c7ab0426ee58cbc1e9c449d
https://doi.org/10.1146/annurev-neuro-100119-110036
https://doi.org/10.1146/annurev-neuro-100119-110036
Autor:
Brian Wheatman, Randal Burns
Publikováno v:
2021 IEEE International Conference on Big Data (Big Data).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d9f42795640baaac9ea772d4761428ed
https://doi.org/10.1109/bigdata52589.2021.9671836
https://doi.org/10.1109/bigdata52589.2021.9671836
Autor:
Joshua T. Vogelstein, V.D. Calhoun, Carey E. Priebe, Michael P. Milham, A Loftus, Rex E. Jung, Sephira G. Ryman, Richard C. Craddock, William Gray Roncal, Brian Caffo, Ross Lawrence, Daniel S. Margulies, Disa Mhembere, Vikram Chandrashekhar, Eric W. Bridgeford, Zuo X-N., Gregory Kiar, Randal Burns
Connectomics—the study of brain networks—provides a unique and valuable opportunity to study the brain. However, research in human connectomics, accomplished via Magnetic Resonance Imaging (MRI), is a resource-intensive practice: typical analysis
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9e79dccecf66ff0aeee1c9fbd70a6c3a
https://doi.org/10.1101/2021.11.01.466686
https://doi.org/10.1101/2021.11.01.466686
Publikováno v:
KDD
We present methods to serialize and deserialize gradient-boosted trees and random forests that optimize inference latency when models are not loaded into memory. This arises when models are larger than memory, but also systematically when models are
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ba8a7f7e0ef61ccdb9d1ad0314e9d963
https://doi.org/10.1145/3447548.3467368
https://doi.org/10.1145/3447548.3467368
Publikováno v:
EuroSys
The advent of Persistent Memory (PM) devices enables systems to actively persist information at low costs, including program state traditionally in volatile memory. However, this trend poses a reliability challenge in which multiple classes of soft f
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::83804449b181bd3a694de650d53e03d3
https://doi.org/10.1145/3447786.3456252
https://doi.org/10.1145/3447786.3456252
Autor:
Joshua T. Vogelstein, Carey E. Priebe, Meghana Madhyastha, J.D. Browne, Veronika Strnadová-Neeley, Gongkai Li, Randal Burns
Publikováno v:
Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.
Together with the curse of dimensionality, nonlinear dependencies in large data sets persist as major challenges in data mining tasks. A reliable way to accurately preserve nonlinear structure is to compute geodesic distances between data points. Man
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4912ee75806c5c6049eda10388b20964
https://doi.org/10.1145/3394486.3403094
https://doi.org/10.1145/3394486.3403094
Autor:
Randal Burns
Publikováno v:
eLearn. 2020
After a decade of struggle to help students install and launch machine virtual machines in the cloud, the author migrated his computer science course to the Gigantum data science platform, which automates the delivery of complex software configuratio
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b0b2e9dc134b3c7e629cfa2bf3bb542a
https://doi.org/10.1145/3409311.3403400
https://doi.org/10.1145/3409311.3403400
Autor:
George S. Plummer, Isaac H. Bianco, Andrew Champion, Arthur W. Wetzel, David G. C. Hildebrand, Joshua T. Vogelstein, Marcelo Cicconet, Russel Torres, Alexander F. Schier, Owen Randlett, Randal Burns, Jeff W. Lichtman, Wei-Chung Allen Lee, Won-Ki Jeong, Stephan Saalfeld, Alexander D. Baden, Jungmin Moon, Florian Engert, Tran Minh Quan, Ruben Portugues, Woohyuk Choi, Kunal Lillaney, Brett J. Graham
Publikováno v:
Nature. 545:345-349
Investigating the dense meshwork of wires and synapses that form neuronal circuits is possible with the high resolution of serial-section electron microscopy (ssEM)1. However, the imaging scale required to comprehensively reconstruct axons and dendri
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bfd088a5336034ee330682e74f068603
https://doi.org/10.1038/nature22356
https://doi.org/10.1038/nature22356