Nested Neural Networks: A Novel Approach to Flexible and Deep Learning Architectures

In this paper, we introduce a novel neural network architecture termed Nested Neural Networks (NNN), which incorporates nested layers within a more complex overarching structure. The architecture leverages the concept of internal and external conditions, akin to linguistic structures like Future Perfect and Future Perfect Progressive in English grammar, to create a flexible and deep model. This architecture allows the network to handle complex data patterns with enhanced computational efficiency and memory savings. We demonstrate the effectiveness of NNNs through experiments on benchmark datasets, showcasing their ability to outperform traditional models in terms of accuracy, training efficiency, and adaptability.