Adversarially-regularized mixed effects deep learning (ARMED) models for
improved interpretability, performance, and generalization on clustered data

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Adversarially-regularized mixed effects deep learning (ARMED) models for improved interpretability, performance, and generalization on clustered data

23 February 2022

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Papers citing "Adversarially-regularized mixed effects deep learning (ARMED) models for improved interpretability, performance, and generalization on clustered data"

7 / 7 papers shown

Title
Fairness-enhancing mixed effects deep learning improves fairness on in- and out-of-distribution clustered (non-iid) data Adam Wang Son Nguyen A. Montillo FedML 63 1 0 31 Dec 2024
Enabling Mixed Effects Neural Networks for Diverse, Clustered Data Using Monte Carlo Methods Andrej Tschalzev Paul Nitschke Lukas Kirchdorfer Stefan Lüdtke Christian Bartelt Heiner Stuckenschmidt 81 0 0 01 Jul 2024
Enhancing MRI-Based Classification of Alzheimer's Disease with Explainable 3D Hybrid Compact Convolutional Transformers Arindam Majee Avisek Gupta S. Raha Swagatam Das MedIm 62 2 0 24 Mar 2024
A Survey on Domain Generalization for Medical Image Analysis Ziwei Niu Shuyi Ouyang Shiao Xie Yen-wei Chen Lanfen Lin OOD LM&MA 90 5 0 07 Feb 2024
Domain Generalization for Medical Image Analysis: A Survey Jee Seok Yoon Kwanseok Oh Yooseung Shin Maciej A Mazurowski Heung-Il Suk LM&MA OOD 87 13 0 05 Oct 2023
UQ-ARMED: Uncertainty quantification of adversarially-regularized mixed effects deep learning for clustered non-iid data A. Treacher K. Nguyen Dylan A. Owens D. Heitjan A. Montillo FedML 19 0 0 29 Nov 2022
Adversarial Discriminative Domain Adaptation Eric Tzeng Judy Hoffman Kate Saenko Trevor Darrell GAN OOD 296 4,685 0 17 Feb 2017