RiteeshGrandhi/sciclaimeval-shared-task huggingface dataset:
Claim: We see that p-dLDS broadly outperforms existing methods in all metrics and significantly improves inference for decomposed models.
Context: Table 1 summarizes our quantitative evaluations on three metrics: 1) the mean squared error (MSE) between the learned and ground truth latent dynamics, 2) the MSE between the inferred and true switch rate to determine agreement of the discrete switching behavior,
and 3) the 100-step inference R^{2} to demonstrate that the learned system generalizes beyond a single step on held-out data. (See Appendix D for mathematical definitions).
Caption: Table 1: Metrics for synthetic dynamical systems. Bold means best performance. (\uparrow) indicates higher score is better while (\downarrow) indicates that lower is better. ✗ indicates that value diverged towards -\infty . Switch events for decomposed models are defined as times where the active set of DOs change from the previous time step.