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Missing data in time series is a pervasive problem that puts obstacles in the way of advanced analysis.
Cloze Procedure: A new tool for measuring readability
W. L. Taylor · 1953
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Inference and missing data
Donald B. Rubin · 1976
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Inference and missing data
Donald B. Rubin · 1976
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Craig F. Ansley and Robert Kohn · 1984
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Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals
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Edgar Acuna and Caroline Rodriguez · 2004
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D. Kreindler and C. Lumsden · 2006
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Unifying user-based and item-based collaborative filtering approaches by similarity fusion
Jun Wang, Arjen P. de Vries, and Marcel J. T. Reinders · 2006
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Methods for the estimation of missing values in time series, 2006
D. S. Fung · 2006
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Missing data analysis: making it work in the real world
J. Graham · 2009
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Bias and efficiency of multiple imputation compared with complete-case analysis for missing covariate values
Ian White and John Carlin · 2010
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Multiple imputation by chained equations: what is it and how does it work?
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Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012
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Combining multiple imputation and meta-analysis with individual participant data
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Improving multi-step prediction of learned time series models
Arun Venkatraman, Martial Hebert, and J.. Bagnell · 2015
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
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ST-MVL: Filling missing values in geo-sensory time series data
Xiuwen Yi, Yu Zheng, Junbo Zhang, and Tianrui Li · 2016
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Temporal regularized matrix factorization for high-dimensional time series prediction
Hsiang-Fu Yu, Nikhil Rao, and Inderjit S Dhillon · 2016
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Improved techniques for training gans
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, Xi Chen, and Xi Chen · 2016
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Jimmy Ba, J. Kiros, and Geoffrey E. Hinton · 2016
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Deep residual learning for image recognition
Kaiming He, X. Zhang, Shaoqing Ren, and Jian Sun · 2016
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin · 2017
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Cautionary tales on air-quality improvement in beijing
Shuyi Zhang, Bin Guo, Anlan Dong, Jing He, Ziping Xu, and S. Chen · 2017
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UCI machine learning repository, 2017
Dheeru Dua and Casey Graff · 2017
CDSA: cross-dimensional self-attention for multivariate, geo-tagged time series imputation
Jiawei Ma, Zheng Shou, Alireza Zareian, Hassan Mansour, Anthony Vetro, and Shih-Fu Chang · 2019
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BERT: Pre-training of deep bidirectional transformers for language understanding
J. Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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XLNet: Generalized autoregressive pretraining for language understanding
Z. Yang, Zihang Dai, Yiming Yang, J. Carbonell, R. Salakhutdinov, and Quoc V. Le · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
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GAMIN: Generative adversarial multiple imputation network for highly missing data
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BRITS: Bidirectional recurrent imputation for time series
Wei Cao, Dong Wang, Jian Li, Hao Zhou, Lei Li, and Yitan Li · 2018
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GAIN: Missing data imputation using generative adversarial nets
Jinsung Yoon, James Jordon, and Mihaela van der Schaar · 2018
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Multivariate time series imputation with generative adversarial networks
Yonghong Luo, Xiangrui Cai, Ying ZHANG, Jun Xu, and Yuan xiaojie · 2018
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Recurrent neural networks for multivariate time series with missing values
Zhengping Che, S. Purushotham, Kyunghyun Cho, D. Sontag, and Y. Liu · 2018
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Gaussian process prior variational autoencoders
Francesco Paolo Casale, Adrian Dalca, Luca Saglietti, Jennifer Listgarten, and Nicolo Fusi · 2018
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DISAN: Directional self-attention network for rnn/cnn-free language understanding
Tao Shen, Tianyi Zhou, Guodong Long, Jing Jiang, Shirui Pan, and Chengqi Zhang · 2018
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Seongwook Yoon and Sanghoon Sull · 2020
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Mcflow: Monte carlo flow models for data imputation
Trevor W. Richardson, Wencheng Wu, Lei Lin, Beilei Xu, and Edgar A. Bernal · 2020
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Handling incomplete heterogeneous data using vaes
Alfredo Nazábal, Pablo M. Olmos, Zoubin Ghahramani, and Isabel Valera · 2020
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Attention-based learning for missing data imputation in holoclean
Richard Wu, Aoqian Zhang, Ihab Ilyas, and Theodoros Rekatsinas · 2020
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GP-VAE: Deep probabilistic time series imputation
Vincent Fortuin, Dmitry Baranchuk, Gunnar Raetsch, and Stephan Mandt · 2020
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Sparse gaussian process variational autoencoders
M. Ashman, Jonathan So, Will Tebbutt, Vincent Fortuin, Michael Pearce, and Richard E. Turner · 2020
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Fast and accurate deep bidirectional language representations for unsupervised learning
Joongbo Shin, Yoonhyung Lee, Seunghyun Yoon, and Kyomin Jung · 2020
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Forecasting loss of signal in optical networks with machine learning
Wenjie Du, David Côté, Chris Barber, and Yan Liu · 2021
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Imputation techniques for the reconstruction of missing interconnected data from higher educational institutions
Renato Bruni, Cinzia Daraio, and Davide Aureli · 2021
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MAIN: multihead-attention imputation networks
Spyridon Mouselinos, Kyriakos Polymenakos, Antonis Nikitakis, and Konstantinos Kyriakopoulos · 2021
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Longitudinal variational autoencoder
Siddharth Ramchandran, Gleb Tikhonov, Kalle Kujanpää, Miika Koskinen, and Harri Lähdesmäki · 2021
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Missing value imputation on multidimensional time series
Parikshit Bansal, Prathamesh Deshpande, and Sunita Sarawagi · 2021
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NRTSI: Non-recurrent time series imputation
Siyuan Shan and Junier B. Oliva · 2021
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Informer: Beyond efficient transformer for long sequence time-series forecasting
Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang · 2021
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