Scene flow estimation is an essential ingredient for a variety of real-world applications, especially for autonomous agents, such as self-driving cars and robots.

Transformers have achieved great success in machine learning applications.

In the following subsections, we provide theoretical derivations. In this subsection, we provide a formal description of the consistency property of score matching. Assumption A.4. (Compactness) The parameter space is compact. Assumption A.5. (Identifiability) There exists a set of parameters A.3 are the conditions that ensure A.7 lead to the uniform convergence property [ In the following Lemma A.9 and Proposition A.10, we examine the sufficient condition for We show that the sufficient conditions stated in Lemma A.9 can be satisfied using the Figure A1: An illustration of the relationship between the variables discussed in Proposition 4.1, Lemma A.12, and Lemma A.13. The properties of KL divergence and Fisher divergence presented in the last two rows are derived in Lemmas A.12 In this section, we provide formal derivations for Proposition 4.1, Lemma A.12, and Lemma A.13. Based on Remark A.14, the following holds: D In this section, we elaborate on the experimental setups and provide the detailed configurations for the experiments presented in Section 5 of the main manuscript.

Our experimental results demonstrate that this approach significantly improves the training efficiency as compared to maximum likelihood training.

The development of efficient machine learning models for molecular systems representation is becoming crucial in scientific research.

Structured Transform(SS1), an expressive and GPU-friendly operator that accelerates inference.