In this presentation, we discuss a classical result in optimization involving Radon measures, known as the representer theorem. This theorem plays a central role in the mean-field formulation of neural network training. In addition, it is fundamental to the moment method for identifying initial sources in heat equations. We will explore both applications from theoretical and practical perspectives.