Manifold Oblique Random Forests: Towards Closing the Gap on Convolutional Deep Networks-Reference-Cited by-同舟云学术

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Manifold Oblique Random Forests: Towards Closing the Gap on Convolutional Deep Networks

Published:2023-02-03 Issue:1 Volume:5 Page:77-96
ISSN:2577-0187
Container-title:SIAM Journal on Mathematics of Data Science
language:en
Short-container-title:SIAM Journal on Mathematics of Data Science

Author:

Li Adam¹^ORCID,Perry Ronan²,Huynh Chester¹,Tomita Tyler M.²,Mehta Ronak³,Arroyo Jesus³,Patsolic Jesse³,Falk Ben³,Sarma Sridevi¹,Vogelstein Joshua⁴

Affiliation:

1. Department of Biomedical Engineering, and Institute for Computational Medicine, Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD 21202 USA.

2. Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21202 USA.

3. Center for Imaging Science, Johns Hopkins University, Baltimore, MD 21202 USA.

4. Department of Biomedical Engineering, Center for Imaging Science, and Institute for Computational Medicine, Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD 21202 USA.

Funder

Whitaker Fellowship

Chateaubriand Fellowship

Achievement Rewards for College Scientists Foundation

Defense Advanced Research Projects Agency

National Institutes of Health

Microsoft Research

National Science Foundation

Publisher

Society for Industrial & Applied Mathematics (SIAM)

Subject

General Medicine

Reference27 articles.

1. Shape Quantization and Recognition with Randomized Trees

2. Generalized random forests

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