Understanding the Principles of Recursive Neural networks: A Generative Approach to Tackle Model Complexity

TL;DR

This paper analyzes the principles of Recursive Neural Networks to understand their computational power and introduces an approximate second-order stochastic learning algorithm that improves training efficiency and robustness.

Contribution

It provides a theoretical analysis of recursive neural networks and proposes a novel adaptive learning algorithm that reduces computational costs and addresses vanishing gradients.

Findings

The new algorithm dynamically adapts learning rates during training.

It operates effectively in both online and batch modes.

Demonstrated improved training efficiency on real-world data.

Abstract

Recursive Neural Networks are non-linear adaptive models that are able to learn deep structured information. However, these models have not yet been broadly accepted. This fact is mainly due to its inherent complexity. In particular, not only for being extremely complex information processing models, but also because of a computational expensive learning phase. The most popular training method for these models is back-propagation through the structure. This algorithm has been revealed not to be the most appropriate for structured processing due to problems of convergence, while more sophisticated training methods enhance the speed of convergence at the expense of increasing significantly the computational cost. In this paper, we firstly perform an analysis of the underlying principles behind these models aimed at understanding their computational power. Secondly, we propose an approximate second order stochastic learning algorithm. The proposed algorithm dynamically adapts the learning rate throughout the training phase of the network without incurring excessively expensive computational effort. The algorithm operates in both on-line and batch modes. Furthermore, the resulting learning scheme is robust against the vanishing gradients problem. The advantages of the proposed algorithm are demonstrated with a real-world application example.