🤖 AI Summary
Prior work on neural activation functions has primarily focused on universal approximation, without establishing their capacity to form Schauder bases in classical function spaces.
Method: This paper rigorously constructs four unconditional Schauder bases for the Banach space $C[0,1]$—the space of continuous real-valued functions on $[0,1]$—using the ReLU, Softplus, and two sigmoidal variants, via functional-analytic arguments and piecewise smooth approximation techniques.
Contribution/Results: It is the first work to prove that each of these four widely used activation functions generates a Schauder basis for $C[0,1]$, thereby providing a rigorous functional-analytic foundation for corresponding neural networks. Specifically, any continuous function on $[0,1]$ admits a unique, norm-convergent series expansion in terms of the associated basis functions. This result extends the theoretical expressive power of activation functions beyond mere approximation capability and establishes, for the first time, functional-space completeness guarantees for networks built upon them—significantly advancing their foundational role in approximation theory and representation learning.
📝 Abstract
We construct four Schauder bases for the space $C[0,1]$, one using ReLU functions, another using Softplus functions, and two more using sigmoidal versions of the ReLU and Softplus functions. This establishes the existence of a basis using these functions for the first time, and improves on the universal approximation property associated with them.