CoNSoLe: Convex Neural Symbolic Learning

Understand

Learning the underlying equation from data is a fundamental problem in many disciplines.

• Recent advances rely on Neural Networks (NNs) but do not provide theoretical guarantees in obtaining the exact equations owing to the non-convexity of NNs.
• In this paper, we propose Convex Neural Symbolic Learning (CoNSoLe) to seek convexity under mild conditions.
• The main idea is to decompose the recovering process into two steps and convexify each step.