163 citations · 704 across the 74 of their papers we have counts for
7 papers · 1 filter
On-line learning of dynamic systems: sparse regression meets Kalman filtering
Gianluigi Pillonetto, Akram Yazdani, Aleksandr Aravkin
Learning governing equations from data is central to understanding the behavior of physical systems across diverse scientific disciplines, including physics, biology, and engineeri…
Sparse and nonparametric estimation of equations governing dynamical systems with applications to biology
G. Pillonetto, A. Giaretta, A. Aravkin +2
Data-driven discovery of model equations is a powerful approach for understanding the behavior of dynamical systems in many scientific fields. In particular, the ability to learn m…
Deep networks for system identification: a Survey
Gianluigi Pillonetto, Aleksandr Aravkin, Daniel Gedon +3
Deep learning is a topic of considerable current interest. The availability of massive data collections and powerful software resources has led to an impressive amount of results i…
Spatiotemporal k-means
Olga Dorabiala, Devavrat Vivek Dabke, Jennifer Webster +2
Spatiotemporal data is increasingly available due to emerging sensor and data acquisition technologies that track moving objects. Spatiotemporal clustering addresses the need to ef…
Data-Driven Aerospace Engineering: Reframing the Industry with Machine Learning
Steven L. Brunton, J. Nathan Kutz, Krithika Manohar +9
Data science, and machine learning in particular, is rapidly transforming the scientific and industrial landscapes. The aerospace industry is poised to capitalize on big data and m…
Accelerating Hessian-free optimization for deep neural networks by implicit preconditioning and sampling
Tara N. Sainath, Lior Horesh, Brian Kingsbury +2
Hessian-free training has become a popular parallel second or- der optimization technique for Deep Neural Network training. This study aims at speeding up Hessian-free training, bo…