mlsanityData to 14 Oct 2026

Processing of missing data by neural networks

arXiv 1805.07405 ↗First posted 18 May 2018cs.LG stat.MLcited by 37

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Abstract (arXiv)

We propose a general, theoretically justified mechanism for processing missing data by neural networks. Our idea is to replace typical neuron's response in the first hidden layer by its expected value. This approach can be applied for various types of networks at minimal cost in their modification. Moreover, in contrast to recent approaches, it does not require complete data for training. Experimental results performed on different types of architectures show that our method gives better results than typical imputation strategies and other methods dedicated for incomplete data.

Key facts. Cited by 37 papers in the map (data through 14 Oct 2026). First posted on arXiv 18 May 2018.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 6; 2022: 11; 2023: 4; 2024: 6; 2025: 7; 2026: 3.

37 papers.

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  1. 2021Spatial Aggregation and Temporal Convolution Networks for Real-time Krigingcited by 27
  2. 2022Missing Data Imputation and Acquisition with Deep Hierarchical Models and Hamiltonian Monte Carlocited by 19
  3. 2021Decoupling Long- and Short-Term Patterns in Spatiotemporal Inferencecited by 15
  4. 2022Missing Value Estimation using Clustering and Deep Learning within Multiple Imputation Frameworkcited by 13
  5. 2023Active Visual Exploration Based on Attention-Map Entropycited by 13
  6. 2022EGG-GAE: scalable graph neural networks for tabular data imputationcited by 7
  7. 2024LITE: Modeling Environmental Ecosystems with Multimodal Large Language Modelscited by 7
  8. 2024AdaGlimpse: Active Visual Exploration with Arbitrary Glimpse Position and Scalecited by 7
  9. 2024Review for Handling Missing Data with special missing mechanismcited by 7
  10. 2021Economic Nowcasting with Long Short-Term Memory Artificial Neural Networks (LSTM)cited by 5
  11. 2022GEDI: A Graph-based End-to-end Data Imputation Frameworkcited by 5
  12. 2025Robust Simulation-Based Inference under Missing Data via Neural Processescited by 5
25 more
  1. 2021MisConv: Convolutional Neural Networks for Missing Datacited by 4
  2. 2022Modeling Continuous Time Sequences with Intermittent Observations using Marked Temporal Point Processescited by 3
  3. 2025Deep learning with missing datacited by 3
  4. 2021Probing the Effect of Selection Bias on Generalization: A Thought Experimentcited by 2
  5. 2022Forecasting Loss of Signal in Optical Networks with Machine Learningcited by 2
  6. 2022No imputation without representationcited by 2
  7. 2022Irregularly-Sampled Time Series Modeling with Spline Networkscited by 2
  8. 2023Training of Neural Networks with Uncertain Data: A Mixture of Experts Approachcited by 2
  9. 2024SEMF: Supervised Expectation-Maximization Framework for Predicting Intervalscited by 2
  10. 2025HI-PMK: A Data-Dependent Kernel for Incomplete Heterogeneous Data Representationcited by 2
  11. 2021MAIN: Multihead-Attention Imputation Networkscited by 1
  12. 2022PROMISSING: Pruning Missing Values in Neural Networkscited by 1
  13. 2023Towards Better Modeling with Missing Data: A Contrastive Learning-based Visual Analytics Perspectivecited by 1
  14. 2024Data Imputation by Pursuing Better Classification: A Supervised Kernel-Based Methodcited by 1
  15. 2022Ordinal-Quadruplet: Retrieval of Missing Classes in Ordinal Time Seriescited by 0
  16. 2022Radial Spike and Slab Bayesian Neural Networks for Sparse Data in Ransomware Attackscited by 0
  17. 2023Regression with Sensor Data Containing Incomplete Observationscited by 0
  18. 2024Enhancing Missing Data Imputation through Combined Bipartite Graph and Complete Directed Graphcited by 0
  19. 2025Learning Over Dirty Data with Minimal Repairscited by 0
  20. 2025RelMap: Reliable Spatiotemporal Sensor Data Visualization via Imputative Spatial Interpolationcited by 0
  21. 2025Meta-Imputation Balanced (MIB): An Ensemble Approach for Handling Missing Data in Biomedical Machine Learningcited by 0
  22. 2025An Interdisciplinary and Cross-Task Review on Missing Data Imputationcited by 0
  23. 2026Theoretical Analysis of Measure Consistency Regularization for Partially Observed Datacited by 0
  24. 2026Liquid Gated Attentioncited by 0
  25. 2026Integrated Imputation-Classification for Supervised Learning with Missing Datacited by 0