Research map / Probabilistic and scientific modeling / Dynamical systems
Bayesian Continuous-Time Tucker Decomposition
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Model tensor-core as time-varying GP function with SDE state-space inference (BCTT) vs. baselines | Tucker decomposition · MovieLens, AdsClicks, DBLP (RMSE/MAE, R in {3,5,7,9}) | RMSE and MAE | Helped |
| BCTT's AdsClicks MAE vs. CT-CP at R=9 (exception case) | Tucker decomposition with GP tensor-core · AdsClicks (R=9) | MAE | Mixed |
| Replace discrete time steps/polynomial splines with GP-based continuous time coefficients | Tensor-core temporal dynamics · DBLP structural analysis (R=7) | learned tensor-core structure over time | Helped |
Closest papers by meaning
- Functional Bayesian Tucker Decomposition for Continuous-indexed Tensor Data (ICLR 2024 · similarity 0.91)
- Partially Observed Trajectory Inference using Optimal Transport and a Dynamics Prior (ICLR 2025 · similarity 0.85)
- Functional Complexity-adaptive Temporal Tensor Decomposition (NeurIPS 2025 · similarity 0.85)
- Efficient Nonparametric Tensor Decomposition for Binary and Count Data (AAAI 2024 · similarity 0.85)
- BayOTIDE: Bayesian Online Multivariate Time Series Imputation with Functional Decomposition (ICML 2024 · similarity 0.84)
- STACI: Spatio-Temporal Aleatoric Conformal Inference (NeurIPS 2025 · similarity 0.84)
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