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Deep Mixed Effect Model using Gaussian Processes: A Personalized and
  Reliable Prediction for Healthcare

Deep Mixed Effect Model using Gaussian Processes: A Personalized and Reliable Prediction for Healthcare

5 June 2018
Ingyo Chung
Saehoon Kim
Juho Lee
Kwang Joon Kim
Sung Ju Hwang
Eunho Yang
    BDL
    FedML
ArXivPDFHTML

Papers citing "Deep Mixed Effect Model using Gaussian Processes: A Personalized and Reliable Prediction for Healthcare"

7 / 7 papers shown
Title
Reverse Survival Model (RSM): A Pipeline for Explaining Predictions of
  Deep Survival Models
Reverse Survival Model (RSM): A Pipeline for Explaining Predictions of Deep Survival Models
Mohammadreza Rezaei
Reza Saadati Fard
Ebrahim Pourjafari
Navid Ziaei
Amir Sameizadeh
M. Shafiee
M. Alavinia
M. Abolghasemian
Nick Sajadi
33
1
0
27 Oct 2022
Personalized Federated Learning with Adaptive Batchnorm for Healthcare
Personalized Federated Learning with Adaptive Batchnorm for Healthcare
Wang Lu
Jindong Wang
Yiqiang Chen
Xin Qin
Renjun Xu
Dimitrios Dimitriadis
Tao Qin
FedML
OOD
24
61
0
01 Dec 2021
UNITE: Uncertainty-based Health Risk Prediction Leveraging Multi-sourced
  Data
UNITE: Uncertainty-based Health Risk Prediction Leveraging Multi-sourced Data
Chacha Chen
Junjie Liang
Fenglong Ma
Lucas Glass
Jimeng Sun
Cao Xiao
21
26
0
22 Oct 2020
Multitask learning and benchmarking with clinical time series data
Multitask learning and benchmarking with clinical time series data
Hrayr Harutyunyan
Hrant Khachatrian
David C. Kale
Greg Ver Steeg
Aram Galstyan
OOD
AI4TS
35
857
0
22 Mar 2017
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Chelsea Finn
Pieter Abbeel
Sergey Levine
OOD
383
11,700
0
09 Mar 2017
Recurrent Neural Networks for Multivariate Time Series with Missing
  Values
Recurrent Neural Networks for Multivariate Time Series with Missing Values
Zhengping Che
S. Purushotham
Kyunghyun Cho
David Sontag
Yan Liu
AI4TS
246
1,900
0
06 Jun 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in
  Deep Learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCV
BDL
285
9,145
0
06 Jun 2015
1