Research map / Time series / Causal and generative time series
Latent Ordinary Differential Equations for Irregularly-Sampled Time Series
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| replace RNN encoder with ODE-RNN encoder (ablation) | Latent ODE · MuJoCo interpolation 10% | MSE: 2.477 -> 0.360 | Helped |
| replace RNN encoder with ODE-RNN encoder (ablation) | Latent ODE · PhysioNet interpolation | MSE: 3.907 -> 2.118 | Helped |
| continuous ODE hidden dynamics between observations (ODE-RNN) vs RNN | autoregressive RNN · MuJoCo interpolation 10% | MSE: 2.454 -> 1.647 | Helped |
| ODE-RNN vs RNN | autoregressive RNN · MuJoCo extrapolation | MSE: 7.259 -> 13.508 | Hurt |
| ODE-based models vs GRU-D | mortality classifier · PhysioNet mortality | AUC: 0.818 -> 0.833 | No clear effect |
| add Poisson process likelihood on observation times (ablation) | Latent ODE · PhysioNet mortality | AUC: 0.829 -> 0.826 | No clear effect |
| Latent ODE vs ODE-RNN classifier | per-time-point classifier · Human Activity | accuracy: 0.829 -> 0.846 | Helped |
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- Neural Differential Equations for Learning to Program Neural Nets Through Continuous Learning Rules (NeurIPS 2022 · similarity 0.88)
- Stable Neural Stochastic Differential Equations in Analyzing Irregular Time Series Data (ICLR 2024 · similarity 0.88)
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