Zhengmin Kong

Kronecker Generative Networks: A General Neural Architecture for Parameter-Efficient Learning Across Classification Tasks

Modern neural networks derive much of their effectiveness from rich connectivity patterns. Yet, existing architectures often fix the topology at either the sparse or dense extremes, thereby limiting structural flexibility and analysis. We propose Kronecker Generative Networks (KGNs), an algebraic fr…