Hardness of Learning Neural Networks under the Manifold Hypothesis

TL;DR

This paper examines the computational hardness of learning neural networks when data lies on low-dimensional manifolds, showing that curvature and volume assumptions critically influence learnability.

Contribution

It extends hardness results to geometric data manifolds and identifies conditions under which learning becomes feasible, bridging theoretical and empirical insights.

Findings

Hardness persists for manifolds with bounded curvature.

Additional volume assumptions enable efficient learning.

Empirical exploration of intermediate manifold regimes.

Abstract

The manifold hypothesis presumes that high-dimensional data lies on or near a low-dimensional manifold. While the utility of encoding geometric structure has been demonstrated empirically, rigorous analysis of its impact on the learnability of neural networks is largely missing. Several recent results have established hardness results for learning feedforward and equivariant neural networks under i.i.d. Gaussian or uniform Boolean data distributions. In this paper, we investigate the hardness of learning under the manifold hypothesis. We ask which minimal assumptions on the curvature and regularity of the manifold, if any, render the learning problem efficiently learnable. We prove that learning is hard under input manifolds of bounded curvature by extending proofs of hardness in the SQ and cryptographic settings for Boolean data inputs to the geometric setting. On the other hand, we show that additional assumptions on the volume of the data manifold alleviate these fundamental limitations and guarantee learnability via a simple interpolation argument. Notable instances of this regime are manifolds which can be reliably reconstructed via manifold learning. Looking forward, we comment on and empirically explore intermediate regimes of manifolds, which have heterogeneous features commonly found in real world data.