2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDMLSP-45.4
Paper Title Leaky Integrator Dynamical Systems and Reachable Sets
Authors Brian Whiteaker, Peter Gerstoft, University of California, San Diego, United States
SessionMLSP-45: Performance Bounds
LocationGather.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
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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.