Yinan Huang, Hans Hao-Hsun Hsu, Junran Wang, Bo Dai, Pan Li
This paper introduces Sequential Flow Matching, a Bayesian filtering approach that accelerates real-time sequential prediction by efficiently updating predictive distributions, reducing inference latency compared to traditional methods.
Predicting future events from a stream of data is challenging due to the uncertainty and multiple possible outcomes. Traditional methods often start predictions from scratch, which can be slow and inefficient. This study presents a new approach called Sequential Flow Matching, which uses a technique from statistics called Bayesian filtering to make predictions faster by updating the prediction step-by-step. This method is shown to be as accurate as traditional methods but much quicker, making it suitable for real-time applications.
