Notes on stable learning with piecewise-linear basis functions

04/25/2018

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We discuss technical results on learning function approximations using piecewise-linear basis functions, and analyze their stability and convergence using nonlinear contraction theory.

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Notes on stable learning with piecewise-linear basis functions

Representing Piecewise Linear Functions by Functions with Small Arity

Stable Parametrization of Continuous and Piecewise-Linear Functions

Feasibility of random basis function approximators for modeling and control

Note on universal algorithms for learning theory

Constructing wavelets by welding segments of smooth functions

Corrected subdivision approximation of piecewise smooth functions

The Impacts of Convex Piecewise Linear Cost Formulations on AC Optimal Power Flow

Notes on stable learning with piecewise-linear basis functions

Related Research

Representing Piecewise Linear Functions by Functions with Small Arity

Stable Parametrization of Continuous and Piecewise-Linear Functions

Feasibility of random basis function approximators for modeling and control

Note on universal algorithms for learning theory

Constructing wavelets by welding segments of smooth functions

Corrected subdivision approximation of piecewise smooth functions

The Impacts of Convex Piecewise Linear Cost Formulations on AC Optimal Power Flow