- The motivation referring to the Nyquist-Shannon sampling theorem does not seem to be consistent with the observation of numerical experiments. From the sampling theorem, as long as the sampling frequency is high enough, there won't be any information loss. But the numerical experiments suggest that the non time-aware model performs poorly even when the sampling frequency is the highest. This inconsistency makes the connection to the sampling theorem questionable.
- During the training phase, the sampling step is randomly selected from a log-uniform distribution. It is suggested that this distribution helps stabilize the training process, but no theoretical nor numerical analysis is provided. Some ablation studies using different sampling distribution might provide some insights to the choice.
- In the numerical evaluation, performance is compared across different observation rate. Given any time-step, the time-aware model can provide the appropriate prediction by conditioning on the time-step. But for the model trained with a fixed time-step, the information of the observation time-step does not seem to be adjusted. For example, for a model trained with $\Delta t=1$ms, when evaluating at $\Delta t=2$ms, instead of applying the model once, one might want to apply the model twice to have a more accurate prediction given the knowledge of the doubled time-step. Without doing some kind of adjustments for the baseline models make the fairness of comparisons questionable.
- When trained and evaluated with the same time-step, Figure 5 shows similar performance between the baseline model and the time-aware model. This makes the effectiveness of the proposed methods questionable when the goal is just to achieve good performance. Maybe under some scenarios where the training time-step and evaluation time-step are different, the proposed method might make more sense.
- When trained and evaluated with the same time-step, Figure 5 shows similar performance between the baseline model and the time-aware model. This makes the effectiveness of the proposed methods questionable when the goal is just to achieve good performance. Maybe under some scenarios where the training time-step and evaluation time-step are different, the proposed method might make more sense.