Here, we propose adaptive wavelet distillation (A WD), a method which aims to distill information from a trained neural network into a wavelet transform.

In order to explore the impact of pandemics on people's lives, it is crucial to understand the public's concerns and attitudes towards pandemic-related entities (e.g., drugs, vaccines) on social media.

Explainability is valuable to ensure that the model's forecasts are credible

Adam) and accelerated schemes (e.g.

We thank all reviewers for comments. We are glad to see our work commented as "promising"(R3), "effective"(R6), We address their concerns below. Writing We'll rephrase remarks, e.g."Examples give hints to local behavior of optimizers in deep learning". Q2.a Assumptions We list assumptions (1)-(3) as below: Numerically, we need a small β (e.g 0.3) or large t . We also tried default β with large t, results similar to Fig.3(d).

We propose a new representation for encoding 3D shapes as neural fields.