Combining Neural-Symbolic Hyperdimensional Computing, Hyperbolic Neural Networks and Structured Energy-Based Models

Preamble

In the constantly changing world of machine learning the mission to create more intelligent, efficient and interpretable models has led many researchers to explore the fusion of various computational paradigms.

Today I combined these paradigms: Hyperdimensional Computing, Hyperbolic Neural Networks, Neural-Symbolic Integration and Energy-Based Models with Structured Energy Functions which truly stand out as pivotal innovations.

This comprehensive guide dives into a sophisticated neural-symbolic model designed for digit classification using the Digits dataset illuminating the mathematical foundations, dissecting the code with rich snippets and explanations, exploring practical use cases, highlighting the benefits of such integrations and interpreting the impressive results achieved by the model.

Introduction

Machine learning models, particularly neural networks, have revolutionised various domains by achieving state-of-the-art performance in tasks like image classification, natural language processing and more. However, despite their prowess, neural networks often suffer from a lack of interpretability and struggle with tasks…