Neural Networks Fail to Learn Periodic Functions and How to Fix It
This addresses a specific limitation in neural network design for tasks involving periodic patterns, though it is incremental as it modifies existing activations rather than introducing a new paradigm.
The paper tackled the problem of neural networks failing to learn periodic functions, and the result was a new activation function, x + sin^2(x), that achieved this while maintaining good optimization properties, with experimental validation on temperature and financial data.
Previous literature offers limited clues on how to learn a periodic function using modern neural networks. We start with a study of the extrapolation properties of neural networks; we prove and demonstrate experimentally that the standard activations functions, such as ReLU, tanh, sigmoid, along with their variants, all fail to learn to extrapolate simple periodic functions. We hypothesize that this is due to their lack of a "periodic" inductive bias. As a fix of this problem, we propose a new activation, namely, $x + \sin^2(x)$, which achieves the desired periodic inductive bias to learn a periodic function while maintaining a favorable optimization property of the ReLU-based activations. Experimentally, we apply the proposed method to temperature and financial data prediction.