Notes on stable learning with piecewise-linear basis functions

This work addresses stability issues in function approximation for machine learning, but appears incremental as it builds on existing theory without clear broader impact.

The paper tackles the problem of learning function approximations with piecewise-linear basis functions, analyzing their stability and convergence using nonlinear contraction theory, but does not provide concrete numerical results.