MIDA: Multiple Imputation using Denoising Autoencoders

8 May 2017

Papers citing "MIDA: Multiple Imputation using Denoising Autoencoders"

12 / 12 papers shown

Title
Explainability of Machine Learning Models under Missing Data Tuan L. Vo T. Nguyen Hugo Lewi Hammer Michael A. Riegler P. Halvorsen Pal Halvorsen 64 2 0 29 Jun 2024
Imputation using training labels and classification via label imputation Thu Nguyen Tuan L. Vo P. Halvorsen Michael A. Riegler 32 0 0 28 Nov 2023
Towards Better Modeling with Missing Data: A Contrastive Learning-based Visual Analytics Perspective Laixin Xie Ouyang Yang Long-fei Chen Ziming Wu Quan Li 23 0 0 18 Sep 2023
Transformed Distribution Matching for Missing Value Imputation He Zhao Ke Sun Amir Dezfouli Edwin V. Bonilla 36 19 0 20 Feb 2023
Data Imputation with Iterative Graph Reconstruction J. Zhong Weiwei Ye Ning Gui 11 12 0 06 Dec 2022
Deep Learning in Single-Cell Analysis Dylan Molho Jiayuan Ding Zhaoheng Li Haifang Wen Wenzhuo Tang ... P. Danaher Robert Yang Y. Lei Yuying Xie Jiliang Tang 28 22 0 22 Oct 2022
Differentiable and Scalable Generative Adversarial Models for Data Imputation Yangyang Wu Jun Wang Xiaoye Miao Wei Cao Jianwei Yin SyDa 47 14 0 10 Jan 2022
MIRACLE: Causally-Aware Imputation via Learning Missing Data Mechanisms Trent Kyono Yao Zhang Alexis Bellot M. Schaar CML 47 61 0 04 Nov 2021
Stacked DeBERT: All Attention in Incomplete Data for Text Classification Gwenaelle Cunha Sergio Minho Lee 27 30 0 01 Jan 2020
Blood lactate concentration prediction in critical care patients: handling missing values B. Mamandipoor Matthias Neumayer M. Moz Jens Grossklags 31 5 0 03 Oct 2019
Missing Value Imputation Based on Deep Generative Models Hongbao Zhang P. Xie Eric Xing DiffM 22 20 0 05 Aug 2018
Learning representations for multivariate time series with missing data using Temporal Kernelized Autoencoders F. Bianchi L. Livi Karl Øyvind Mikalsen Michael C. Kampffmeyer Robert Jenssen AI4TS 30 11 0 09 May 2018