Markus Heinonen: Differential equations and deep learning
Speaker: Markus Heinonen (Aalto University)
Title: Differential equations and deep learning
Differential equations describe the evolution of a system’s state, and are widely applied in natural sciences. Deep learning, on the other hand, is based on learning a sequential of the input towards a more complex state. Recently several groups have proposed to connect these, seeminly unconnected, branches of science together by re-imagining neural networks as executing a dynamical system, with pioneering works of Neural ODEs (Chen et al 2018) and deep Gaussian process flows (Hegde et al 2019).
In this talk I will describe how to model and learn high-dimensional ODEs and SDEs as Gaussian processes and neural networks, and how to employ them for deep learning. I will discuss the open problems when moving to dynamical system based machine learning.
PhD Markus Heinonen is an academy research fellow at Aalto University, Finland with focus on Bayesian deep learning, Gaussian processes, dynamical systems and their applications (https://users.aalto.fi/~heinom10)