| Paper ID | MLSP-45.4 |
| Paper Title |
Leaky Integrator Dynamical Systems and Reachable Sets |
| Authors |
Brian Whiteaker, Peter Gerstoft, University of California, San Diego, United States |
| Session | MLSP-45: Performance Bounds |
| Location | Gather.Town |
| Session Time: | Friday, 11 June, 13:00 - 13:45 |
| Presentation Time: | Friday, 11 June, 13:00 - 13:45 |
| Presentation |
Poster
|
| Topic |
Machine Learning for Signal Processing: [MLR-PERF] Bounds on performance |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
Reservoir computers are a fast training variant of recurrent neural networks, excelling at approximation of nonlinear dynamical systems and time series prediction. These machine learning models act as self-organizing nonlinear fading memory filters. While these models benefit from low overall complexity, the matrix computations are a complexity bottleneck. This work applies the controllability matrix of control theory to quickly identify a reduced size replacement reservoir. Given a large, task-effective reservoir matrix, we calculate the rank of the associated controllability matrix. This simple calculation identifies the required rank for a reduced size replacement, resulting in time speed-ups to an already fast deep learning model. Additionally, this rank calculation speaks to the state space reachable set required to model the input data. |
