Spring 2022 Seminars
A complete listing
|Thur, Feb 10||11am - 12pm||Francesco Bullo||University of California, Santa Barbara||Implicit and Recurrent Neural Networks via Contraction Theory||https://nyu.zoom.us/j/97671136897||TBD|
Speaker: Francesco Bullo, University of California, Santa Barbara
Date: Feb 10
Abstract: Basic questions in dynamical neuroscience and machine learning motivate the study of the stability, robustness, entrainment, and computational efficiency properties of neural network models. I will present some elements of a comprehensive contraction theory for neural networks. Using non-Euclidean norms I will review recent advances in analyzing and training a class of recurrent/implicit models.
About the Speaker: Francesco Bullo is a Professor with the Mechanical Engineering Department and the Center for Control, Dynamical Systems and Computation at the University of California, Santa Barbara. He was previously associated with the University of Padova (Laurea degree in Electrical Engineering, 1994), the California Institute of Technology (Ph.D. degree in Control and Dynamical Systems, 1999), and the University of Illinois. He served on the editorial boards of IEEE, SIAM, and ESAIM journals and as IEEE CSS President and SIAM SIAG CST Chair. His research interests focus on network systems and distributed control with application to robotic coordination, power grids and social networks. He is the coauthor of “Geometric Control of Mechanical Systems” (Springer, 2004), “Distributed Control of Robotic Networks” (Princeton, 2009), and "Lectures on Network Systems" (Kindle Direct Publishing, 2021, v1.5). He received best paper awards for his work in IEEE Control Systems, Automatica, SIAM Journal on Control and Optimization, IEEE Transactions on Circuits and Systems, and IEEE Transactions on Control of Network Systems. He is a Fellow of IEEE, IFAC, and SIAM.