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Found 7 papers, 3 papers with code
Shifts in Brain Dynamics and Drivers of Consciousness State Transitions
Notably, our approach showcases the effectiveness of LTI models under UI in capturing large-scale brain dynamic changes and drivers in complex paradigms, such as naturalistic stimulation, which are not conducive to conventional general linear model analysis.
Minimum Structural Sensor Placement for Switched Linear Time-Invariant Systems and Unknown Inputs
First, we provide necessary and sufficient conditions for their structural state and input observability that can be efficiently verified in $O((m(n+p))^2)$, where $n$ is the number of state variables, $p$ is the number of unknown inputs, and $m$ is the number of modes.
A Dynamical Systems Approach for Convergence of the Bayesian EM Algorithm
Out of the recent advances in systems and control (S\&C)-based analysis of optimization algorithms, not enough work has been specifically dedicated to machine learning (ML) algorithms and its applications.
Equilibrium Propagation for Complete Directed Neural Networks
Artificial neural networks, one of the most successful approaches to supervised learning, were originally inspired by their biological counterparts.
Analysis of a Generalized Expectation-Maximization Algorithm for Gaussian Mixture Models: A Control Systems Perspective
The Expectation-Maximization (EM) algorithm is one of the most popular methods used to solve the problem of parametric distribution-based clustering in unsupervised learning.
Network Design for Controllability Metrics
In this setting, we first consider a feasibility problem consisting of tuning the edge weights such that certain controllability properties are satisfied.
Optimization and Control
Convergence of the Expectation-Maximization Algorithm Through Discrete-Time Lyapunov Stability Theory
Furthermore, we propose to assess its convergence as asymptotic stability in the sense of Lyapunov.
