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Interpolation-Prediction Networks for Irregularly Sampled Time Series

ICLR 2019 · Time series › Causal and generative time series · approach: Irregular series and imputation · 30 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use semi-parametric interpolation with three channels (smooth, transient, intensity) before predictionGRU prediction network · MIMIC-III mortalityAUPRCHelped
Use semi-parametric interpolation with three channels before predictionLSTM prediction network · MIMIC-III mortality benchmarkAUPRC: 0.4933 -> 0.5370Helped
Add smooth interpolation and intensity to transients (ablation)Interpolation-Prediction Network · MIMIC-III mortalityAUC: 0.705 \pm 0.009 -> 0.853 \pm 0.007Helped
Add smooth interpolation and intensity to transients (ablation)Interpolation-Prediction Network · MIMIC-III length of stayMedAE: 2.995\pm0.130 -> 2.862\pm0.166Helped
Add transients to smooth interpolation and intensity (ablation)Interpolation-Prediction Network · MIMIC-III mortalityAUC: 0.852 \pm 0.005 -> 0.853 \pm 0.007No clear effect
Add transients to smooth interpolation and intensity (ablation)Interpolation-Prediction Network · MIMIC-III mortalitycross-entropy loss: 0.210 \pm 0.004 -> 0.210 \pm 0.004No clear effect
Combine smooth interpolation and intensity rather than using either alone (ablation)Interpolation-Prediction Network · MIMIC-III mortalityAUC: 0.816 \pm 0.009 / 0.786\pm 0.010 -> 0.852 \pm 0.005Helped
Use intensity alone instead of all three channels (ablation)Interpolation-Prediction Network · MIMIC-III length of stayMedAE: 2.862\pm0.166 -> 2.697\pm0.072Helped
Use intensity and transients without smooth interpolation (ablation)Interpolation-Prediction Network · MIMIC-III length of stayEV score: 0.245\pm0.019 -> 0.290\pm0.010Helped

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