Integrating Random Effects in Deep Neural Networks

Integrating Random Effects in Deep Neural Networks

7 June 2022

Papers citing "Integrating Random Effects in Deep Neural Networks"

11 / 11 papers shown

Title
Scalable Computations for Generalized Mixed Effects Models with Crossed Random Effects Using Krylov Subspace Methods Pascal Kündig Fabio Sigrist 14 0 0 14 May 2025
Online Federation For Mixtures of Proprietary Agents with Black-Box Encoders Xuwei Yang Fatemeh Tavakoli D. B. Emerson Anastasis Kratsios FedML 62 0 0 30 Apr 2025
Integrating Random Effects in Variational Autoencoders for Dimensionality Reduction of Correlated Data Giora Simchoni Saharon Rosset 66 0 0 22 Dec 2024
Probabilistic size-and-shape functional mixed models Fangyi Wang Karthik Bharath Oksana Chkrebtii S. Kurtek 65 0 0 27 Nov 2024
Hamiltonian Monte Carlo Inference of Marginalized Linear Mixed-Effects Models Jinlin Lai Justin Domke Daniel Sheldon 33 0 0 31 Oct 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 29 0 0 01 Jul 2024
Reducing the dimensionality and granularity in hierarchical categorical variables Paul Wilsens Katrien Antonio G. Claeskens 19 0 0 06 Mar 2024
Subject-specific Deep Neural Networks for Count Data with High-cardinality Categorical Features Hangbin Lee I. Ha Changha Hwang Youngjo Lee 16 1 0 18 Oct 2023
A Comparison of Machine Learning Methods for Data with High-Cardinality Categorical Variables Fabio Sigrist 20 5 0 05 Jul 2023
Neural Mixed Effects for Nonlinear Personalized Predictions T. Wörtwein Nicholas B. Allen Lisa B. Sheeber Randy P. Auerbach J. Cohn Louis-Philippe Morency 11 7 0 13 Jun 2023
Machine Learning with High-Cardinality Categorical Features in Actuarial Applications Benjamin Avanzi G. Taylor Melantha Wang Bernard Wong 17 12 0 30 Jan 2023