Max Welling: Quantum, Manifolds and Symmetries in ML

https://www.youtube.com/watch?v=mmDw5glry9w Machine Learning Street Talk -- today we had a fantastic conversation with Professor Max Welling, VP of Technology, Qualcomm Technologies Netherlands B.V. Max is a strong believer in the power of data and computation and its relevance to artificial intelligence. There is a fundamental blank slate paradigm in machine learning, experience and data alone currently rule the roost. Max wants to build a house of domain knowledge on top of that blank slate. Max thinks there are no predictions without assumptions, no generalization without inductive bias. The bias-variance trade-off tells us that we need to use additional human knowledge when data is insufficient. Max Welling has pioneered many of the most sophisticated inductive priors in DL models developed in recent years, allowing us to use Deep Learning with non-Euclidean data i.e. on graphs/topology (a field we now called "geometric deep learning") or allowing network architectures to recognise new symmetries in the data for example gauge or SE(3) equivariance. Max has also brought many other concepts from his physics playbook into ML, for example quantum and even Bayesian approaches.