Gradient descent fails to learn high-frequency functions and modular arithmetic

Rustem Takhanov, Maxat Tezekbayev, Artur Pak, Arman Bolatov, Zhenisbek Assylbekov

Abstract: Classes of target functions containing a large number of approximately orthogonal elements are known to be hard to learn by the Statistical Query algorithms. Recently this classical fact re-emerged in a theory of gradient-based optimization of neural networks. In the novel framework, the hardness of a class is usually quantified by the variance of the gradient with respect to a random choice of a target function. A set of functions of the form \(x\rightarrow ax \bmod p\), where a is taken from \({{\mathbb {Z}}}_p\), has attracted some attention from deep learning theorists and cryptographers recently. This class can be understood as a subset of p-periodic functions on \({{\mathbb {Z}}}\) and is tightly connected with a class of high-frequency periodic functions on the real line. We present a mathematical analysis of limitations and challenges associated with using gradient-based learning techniques to train a high-frequency periodic function or modular multiplication from examples. We highlight that the variance of the gradient is negligibly small in both cases when either a frequency or the prime base p is large. This in turn prevents such a learning algorithm from being successful.