Convex Neural Network

Convex neural networks (CNNs) aim to leverage the advantages of convex optimization—guaranteed global optima and improved stability—within the framework of neural networks. Current research focuses on developing CNN architectures, such as input convex neural networks (ICNNs) and parameter convex neural networks (PCNNs), and algorithms that ensure or approximate convexity, often employing techniques from optimal transport and geometric algebra. This approach offers the potential for enhanced robustness, improved generalization, and provable guarantees on model performance, particularly beneficial for critical applications like energy systems and medical imaging where reliability is paramount.