Adversarially-Regularized Mixed Effects Deep Learning (ARMED) Models Improve Interpretability, Performance, and Generalization on Clustered (non-<i>iid</i>) Data-Reference-Cited by-同舟云学术

Adversarially-Regularized Mixed Effects Deep Learning (ARMED) Models Improve Interpretability, Performance, and Generalization on Clustered (non-iid) Data

Published:2023-07-01 Issue:7 Volume:45 Page:8081-8093
ISSN:0162-8828
Container-title:IEEE Transactions on Pattern Analysis and Machine Intelligence
language:
Short-container-title:IEEE Trans. Pattern Anal. Mach. Intell.

Author:

Nguyen Kevin P.¹^ORCID,Treacher Alex H.¹,Montillo Albert A.¹^ORCID

Affiliation:

1. Lyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, USA

Funder

Lyda Hill Foundation

National Institutes of Health

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Applied Mathematics,Artificial Intelligence,Computational Theory and Mathematics,Computer Vision and Pattern Recognition,Software

Link

http://xplorestaging.ieee.org/ielx7/34/10144448/10016237.pdf?arnumber=10016237

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