Imputation
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Benchmarks
Most implemented
GAIN: Missing Data Imputation using Generative Adversarial Nets
Recurrent Neural Networks for Multivariate Time Series with Missing Values
Deep Learning in Single-Cell Analysis
A Transformer-based Framework for Multivariate Time Series Representation Learning
Unsupervised Data Imputation via Variational Inference of Deep Subspaces
Deep Learning for Multivariate Time Series Imputation: A Survey
Papers
Missing value imputation with adversarial random forests -- MissARF
Handling missing values is a common challenge in biostatistical analyses, typically addressed by imputation methods. We propose a novel, fast, and easy-to-use imputation method called missing value imputation with advers…
Density EstimationImputationMissing ValuesMoTM: Towards a Foundation Model for Time Series Imputation based on Continuous Modeling
Recent years have witnessed a growing interest for time series foundation models, with a strong emphasis on the forecasting task. Yet, the crucial task of out-of-domain imputation of missing values remains largely undere…
Domain GeneralizationImputationMissing ValuesTime SeriesBMFM-DNA: A SNP-aware DNA foundation model to capture variant effects
Large language models (LLMs) trained on text demonstrated remarkable results on natural language processing (NLP) tasks. These models have been adapted to decipher the language of DNA, where sequences of nucleotides act …
ImputationPromoter DetectionLeveraging AI Graders for Missing Score Imputation to Achieve Accurate Ability Estimation in Constructed-Response Tests
Evaluating the abilities of learners is a fundamental objective in the field of education. In particular, there is an increasing need to assess higher-order abilities such as expressive skills and logical thinking. Const…
Data AugmentationImputationDIM-SUM: Dynamic IMputation for Smart Utility Management
Time series imputation models have traditionally been developed using complete datasets with artificial masking patterns to simulate missing values. However, in real-world infrastructure monitoring, practitioners often e…
ImputationManagementMissing ValuesTrustworthy Prediction with Gaussian Process Knowledge Scores
Probabilistic models are often used to make predictions in regions of the data space where no observations are available, but it is not always clear whether such predictions are well-informed by previously seen data. In …
Anomaly DetectionGPRImputationPrediction