Noether Networks: Meta-Learning Useful Conserved Quantities (w/ the authors)

#deeplearning #noether #symmetries This video includes an interview with first author Ferran Alet! Encoding inductive biases has been a long established methods to provide deep networks with the ability to learn from less data. Especially useful are encodings of symmetry properties of the data, such as the convolution's translation invariance. But such symmetries are often hard to program explicitly, and can only be encoded exactly when done in a direct fashion. Noether Networks use Noether's theorem connecting symmetries to conserved quantities and are able to dynamically and approximately enforce symmetry properties upon deep neural networks. OUTLINE: 0:00 - Intro & Overview 18:10 - Interview Start 21:20 - Symmetry priors vs conserved quantities 23:25 - Example: Pendulum 27:45 - Noether Network Model Overview 35:35 - Optimizing the Noether Loss 41:00 - Is the computation graph stable? 46:30 - Increasing the inference time computation 48:45 - Why dynamically modify the model? 55:30 - Experimental Results &