Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification | Paper Citation Record

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification The treatment of missing values and its effect on classifier accuracy

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification Investigating Under and Overfitting in Wasserstein Generative Adversarial Networks

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification Neighborhood-aware autoencoder for missing value imputation

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification What regularized auto-encoders learn from the data-generating distribution

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification Improving sepsis prediction per- formance using conditional recurrent adversarial networks

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification Towards principled methods for training generative adversarial networks

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification Wasserstein generative adversarial networks

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification Sanfilippo, and Girish Dwivedi

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification Estimating conditional transfer entropy in time series using mutual information and nonlinear prediction

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification Neural ma- chine translation by jointly learning to align and translate

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification Multiple imputation with missing data indicators

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification Sched- uled sampling for sequence prediction with recurrent neural networks

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification Curriculum learning

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification An imbalance-aware deep neural network for early prediction of preeclampsia

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification Autoencoders and their applications in machine learn- ing: a survey

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification A novel missing data imputation 70 approach based on clinical conditional generative adversarial networks applied to EHR datasets

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification BEGAN: Boundary Equilibrium Generative Adversarial Networks

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification Multi- indicator water time series imputation with autoregressive generative adversarial networks

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification Deep generative modelling: A comparative review of vaes, gans, nor- malizing flows, energy-based and autoregressive models

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification Generative adversarial networks in time series: A systematic literature review

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification BRITS: bidirectional recurrent imputation for time series

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification Multitask learning

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification Machine and deep learning for longitudinal biomedical data: a review of methods and applications

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification ANODE- GAN: incomplete time series imputation by augmented neural ode- based generative adversarial networks

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification Chawla, Kevin W

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification Neural ordinary differential equations

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification Class-imbalanced deep learning via a class-balanced ensemble

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification Malin, Jon Duke, Wal- ter F

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification Kempa-Liehr

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification On the constrained time-series generation problem

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification Glass, Greg Lever, Jimeng Sun, and Cao Xiao

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification A decoder-only foundation model for time-series forecasting

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification Flexible imputation of missing data

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification Matteson

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification Matteson

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification Density esti- mation using real NVP

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification Adversarial feature learning

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification Principled missing data methods for researchers

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification Adarnn: Adaptive learning and fore- casting of time series

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification Augmented neu- ral odes

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification Atiya, and Firuz Kamalov

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification Predicting sequences of clinical events by using a personalized temporal latent embedding model

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification Wind power time series missing data imputation based on generative adversarial network

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification Towards sus- tainable compressive population health: A gan-based year-by-year im- putation method

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification Completing missing prevalence rates for multiple chronic diseases by jointly lever- aging both intra- and inter-disease population health data correlations

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification Prati, Bartosz Krawczyk, and Francisco Herrera

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification Generative ad- versarial networks for biomedical time series forecasting and imputa- tion

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification Medical multivariate time series imputation and forecasting based on a recurrent conditional wasserstein GAN and attention

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification Fitzmaurice, Nan M

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification Latent space configuration for improved generalization in supervised autoencoder neural networks

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification Missing data imputation framework for bridge structural health monitoring based on slim generative adversarial net- works

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification An unsupervised context-free forecasting method for structural health monitoring by generative adversarial networks with progressive growing and self-attention

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification A data-driven data-augmentation method based on slim- generative adversarial imputation networks for short-term ship-motion attitude prediction

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification A review of Generative Adversarial Networks for Electronic Health Records: applications, evaluation measures and data sources

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification Ghosheh, Jin Li, and Tingting Zhu

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification Research methods in applied settings: An integrated approach to design and anal- ysis

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification Goldberger, Luis A

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification Courville, and Yoshua Bengio

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification Borgwardt, Malte J

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification Groenwold

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification Courville

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification A data imputation method for multivariate time series based on generative adversarial network

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification Yoccoz, and Jean-Michel Gaillard

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification Federated Variational Inference Methods for Structured Latent Variable Models

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification Closing the data gap: A comparative study of missing value imputation algorithms in time series datasets

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification Deep resid- ual learning for image recognition

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification Multivariate time series missing value filling based on trans-gan model

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification TCGAN: convolutional generative adversarial network for time series classification and clustering

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification Interpretable temporal gans for industrial imbalanced multivariate time series simulation and classification

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification Deep con- volutional generative adversarial networks for traffic data imputation encoding time series as images

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification CC-GAIN: clustering and classification-based generative adversarial imputation network for miss- ing electricity consumption data imputation

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification Adversarial Training for Disease Prediction from Electronic Health Records with Missing Data

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This paper cites How to deal with missing data in supervised deep learning? InThe Tenth Inter- national Conference on Learning Representations, ICLR 2022, Virtual Event, April 25-29, 2022.

Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification How to deal with missing data in supervised deep learning? InThe Tenth Inter- national Conference on Learning Representations, ICLR 2022, Virtual Event, April 25-29, 2022

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification Iwana and Seiichi Uchida

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification A wide range of missing impu- tation approaches in longitudinal data: a simulation study and real data analysis

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification Time-series generation by contrastive imitation

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification Re- booting ACGAN: auxiliary classifier gans with stable training

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification LSTM fully convolutional networks for time series classification

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification Training generative adversarial networks with limited data

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification IGANI: iterative generative adver- sarial networks for imputation with application to traffic data

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification Unresolved cited work

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification Mixed data impu- tation using generative adversarial networks

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Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification A survey of miss- ing data imputation using generative adversarial networks

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