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Temporal Logic Point Processes

ICML 2020 · Time series › Causal and generative time series · approach: Generative temporal point processes

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add first-order temporal logic rules as prior knowledge structuring the intensity (Temp Logic vs. data-driven baselines)temporal logic point process · MIMIC-III survival prediction, small data (50 train)accuracy: 0.584 vs 0.530 best baseline (BN)Helped
Add temporal logic rules as prior knowledge (Temp Logic vs. RMTPP)temporal logic point process · MIMIC-III time-to-survival MAEMAE (days): 0.233 vs 0.464 at 50 trainHelped
Add temporal logic rules as prior knowledge (Temp Logic vs. RMTPP) at larger data sizetemporal logic point process · MIMIC-III time-to-survival MAE (4000 train)MAE (days): 0.227 vs 0.399Helped
Add temporal logic rules as prior knowledge in fraud detectiontemporal logic point process · Credit card fraud, 500 trainaccuracy: 0.728 vs 0.650 best baseline (LR)Helped
Transfer learned logic weights from a large dataset (fine-tuned warm start) to a small target dataset (ablation)temporal logic point process · MIMIC-III knowledge transferaccuracy: 0.659 (Transfer2) vs 0.615 (benchmark)Helped
Freeze transferred logic weights (Transfer1) instead of fine-tuning, relative to no transfer (ablation)temporal logic point process · MIMIC-III knowledge transferaccuracy: 0.637 vs 0.615 benchmarkHelped

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