Results 31 to 40 of about 14,502,685 (316)
Sample-Optimal Fourier Sampling in Any Constant Dimension [PDF]
2014 IEEE 55th Annual Symposium on Foundations of Computer Science, 2014 We give an algorithm for l2/l2 sparse recovery from Fourier measurements using O(klog N) samples, matching the lower bound of Do Ba-Indyk-Price-Woodruff'10 for non-adaptive algorithms up to constant factors for any k≤ N1-δ. The algorithm runs in tilde O(N) time.
Piotr Indyk, Michael Kapralov
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Biometrika, 2022 Summary Motivated by the increasing use of kernel-based metrics for high-dimensional and large-scale data, we study the asymptotic behaviour of kernel two-sample tests when the dimension and sample sizes both diverge to infinity. We focus on the maximum mean discrepancy using an isotropic kernel, which includes maximum mean discrepancy ...
Yan, Jian, Zhang, Xianyang
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Small sample sizes: A big data problem in high-dimensional data analysis
Statistical Methods in Medical Research, 2020 In many experiments and especially in translational and preclinical research, sample sizes are (very) small. In addition, data designs are often high dimensional, i.e. more dependent than independent replications of the trial are observed.
F. Konietschke +2 more
semanticscholar +1 more source
Design and implementation of an ultrasonic sensor for rapid monitoring of industrial malolactic fermentation of wines [PDF]
, 2017 Ultrasound is an emerging technology that can be applied to monitor food processes. However, ultrasonic techniques are usually limited to research activities within a laboratory environment and they are not extensively used in industrial processes.
Amer Boixareu, Miguel Ángel +6 more
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VC dimension and distribution-free sample-based testing [PDF]
Proceedings of the 53rd Annual ACM SIGACT Symposium on Theory of Computing, 2021 44 ...
Eric Blais +2 more
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Single sample scoring of molecular phenotypes
BMC Bioinformatics, 2018 Gene set scoring provides a useful approach for quantifying concordance between sample transcriptomes and selected molecular signatures. Most methods use information from all samples to score an individual sample, leading to unstable scores in small data
M. Foroutan +5 more
semanticscholar +1 more source
Learning to Importance Sample in Primary Sample Space [PDF]
Computer graphics forum (Print), 2018 Importance sampling is one of the most widely used variance reduction strategies in Monte Carlo rendering. We propose a novel importance sampling technique that uses a neural network to learn how to sample from a desired density represented by a set of ...
Q. Zheng, Matthias Zwicker
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Children in Greenland: disease patterns and contacts to the health care system [PDF]
International Journal of Circumpolar Health, 2016 Background: Previous studies of Greenlandic children’s disease pattern and contacts to the health care system are sparse and have focused on the primary health care sector.
Marius Kløvgaard +5 more
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Spectral Properties of Circular Piezoelectric Unimorphs
Archives of Acoustics, 2017 The piezoelectric unimorphs are essential resonant components of many oscillating systems including electroacoustic devices. The unimorph spectral properties are namely dependent on geometric dimensions, applied materials and mounting.
Martin PUSTKA, Ladislav PŮST
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Bounding sample size with the Vapnik-Chervonenkis dimension [PDF]
Discrete Applied Mathematics, 1993 The authors give a new proof that a class is learnable in Valiant's P.A.C sense if the Vapnik-Chervonenkis dimension is finite. The new proof provides an improvement of the sample size needed.
John Shawe-Taylor +2 more
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