Abstract. A neural network’s architecture encodes key information and inductive biases that are used to guide its predictions. In this talk, we discuss recent work which leverages the perspective of neural ordinary differential equations to design network architectures that encode the structures of classical mechanics. We examine the cases of both smooth dynamics and non-smooth contact dynamics. The architectures obtained are easy to understand, show excellent performance and data-efficiency on simple benchmark tasks, and are a promising emerging tool for use in robot learning and related areas.
Alexander Terenin is a Postdoctoral Research Associate at the University of Cambridge. He is interested in statistical machine learning, particularly in settings where the data is not fixed, but is gathered interactively by the learning machine. This leads naturally to Gaussian processes and data-efficient interactive decision-making systems such as Bayesian optimization, to areas such as multi-armed bandits and reinforcement learning, and to techniques for incorporating inductive biases and prior information such as symmetries into machine learning models.