NAOMI swMATH ID: 43099 Software Authors: Yukai Liu, Rose Yu, Stephan Zheng, Eric Zhan, Yisong Yue Description: NAOMI: Non-Autoregressive Multiresolution Sequence Imputation. Missing value imputation is a fundamental problem in spatiotemporal modeling, from motion tracking to the dynamics of physical systems. Deep autoregressive models suffer from error propagation which becomes catastrophic for imputing long-range sequences. In this paper, we take a non-autoregressive approach and propose a novel deep generative model: Non-AutOregressive Multiresolution Imputation (NAOMI) to impute long-range sequences given arbitrary missing patterns. NAOMI exploits the multiresolution structure of spatiotemporal data and decodes recursively from coarse to fine-grained resolutions using a divide-and-conquer strategy. We further enhance our model with adversarial training. When evaluated extensively on benchmark datasets from systems of both deterministic and stochastic dynamics. NAOMI demonstrates significant improvement in imputation accuracy (reducing average prediction error by 60 Homepage: https://arxiv.org/abs/1901.10946 Source Code: https://github.com/felixykliu/NAOMI Dependencies: Python Related Software: E2EGAN; GP-VAE; matrix-completion; BRITS; GitHub; imputeTS; missForest; CRAN Cited in: 1 Publication Cited by 4 Authors 1 Aguiar, Rui L. 1 Antunes, Mário 1 Fernandes, Sofia 1 Gomes, Diogo Luís Aguiar Cited in 1 Serial 1 Machine Learning Cited in 1 Field 1 Computer science (68-XX) Citations by Year