GAIN: Missing Data Imputation using Generative Adversarial Nets

7 June 2018

Papers citing "GAIN: Missing Data Imputation using Generative Adversarial Nets"

8 / 308 papers shown

Title
MIWAE: Deep Generative Modelling and Imputation of Incomplete Data Pierre-Alexandre Mattei J. Frellsen SyDa 106 46 0 06 Dec 2018
Rethinking clinical prediction: Why machine learning must consider year of care and feature aggregation Bret A. Nestor Matthew B. A. McDermott Geeticka Chauhan Tristan Naumann Michael C. Hughes Anna Goldenberg Marzyeh Ghassemi OOD 78 35 0 30 Nov 2018
Synthesizing Tabular Data using Generative Adversarial Networks L. Xu K. Veeramachaneni CML GAN 112 256 0 27 Nov 2018
T-CGAN: Conditional Generative Adversarial Network for Data Augmentation in Noisy Time Series with Irregular Sampling Giorgia Ramponi P. Protopapas Marco Brambilla Ryan Janssen AI4TS TTA 87 100 0 20 Nov 2018
Hybrid Generative-Discriminative Models for Inverse Materials Design Phuoc Nguyen T. Tran Sunil R. Gupta Santu Rana Svetha Venkatesh AI4CE PINN 39 6 0 31 Oct 2018
Handling Incomplete Heterogeneous Data using VAEs A. Nazábal Pablo M. Olmos Zoubin Ghahramani Isabel Valera 86 353 0 10 Jul 2018
Variational Autoencoder with Arbitrary Conditioning Oleg Ivanov Michael Figurnov Dmitry Vetrov BDL DRL 109 147 0 06 Jun 2018
Processing of missing data by neural networks Marek Śmieja Lukasz Struski Jacek Tabor Bartosz Zieliñski Przemysław Spurek AI4TS 100 113 0 18 May 2018