Abstract
Artificial neural networks (ANN) were inspired by the architecture and functions of the human brain and have revolutionised the field of artificial intelligence (AI). Inspired by studies on the latent geometry of the brain we posit that an increase in the research and application of hyperbolic geometry in machine learning will lead to increased accuracy, improved feature space representations and more efficient models across a range of tasks. We look at the structure and functions of the human brain, highlighting the alignment between the brain's hierarchical nature and hyperbolic geometry. By examining the brain's complex network of neuron connections and its cognitive processes, we illustrate how hyperbolic geometry plays a pivotal role in human intelligence. Empirical evidence indicates that hyperbolic neural networks outperform Euclidean models for tasks including natural language processing, computer vision and complex network analysis, requiring fewer parameters and exhibiting better generalisation. Despite its nascent adoption, hyperbolic geometry holds promise for improving machine learning models and advancing the field toward AGI.
Community
We are excited to announce the release of our latest research paper, "Hyperbolic Brain Representations", now available on arXiv. This paper explores the potential of hyperbolic geometry in transforming the landscape of artificial neural networks (ANNs) by aligning them more closely with the structure and functions of the human brain.
Artificial Intelligence (AI) and Machine Learning (ML) have long drawn inspiration from the brain's architecture, but up until now, most ANNs operate within the familiar confines of Euclidean geometry. However, recent studies suggest that this may not be the optimal approach for modelling complex and hierarchical structures. The brain's latent geometry is hyperbolic, often offering unique advantages when applied to neural networks, such as improved accuracy, efficiency, and better generalisation.
Our research illustrates that hyperbolic neural networks often outperform Euclidean-based models, particularly in fields like natural language processing, computer vision, and complex network analysis. By emulating the brain's inherent geometry, these models require fewer parameters and achieve better generalisation—critical improvements for AI systems aiming to tackle human-centric tasks. As research in this area advances, we may see a new era of AI models that are not only more accurate and efficient but also more aligned with the fundamental principles of human intelligence.
We've also developed an open-source Python library, Hyperlib, to make it easier for researchers and practitioners to build hyperbolic models. You can explore the library and integrate hyperbolic geometry into your work with a simple pip install hyperlib. Hyperlib simplifies the complex mathematics, allowing you to focus on the potential of non-Euclidean deep learning.
Check out our blog post and start experimenting with Hyperlib!
Blog: https://medium.com/@hyperbolicbrain/hyperbolic-brain-representations-be0368d84c3f
Library: Hyperlib on GitHub Quick Install: pip install hyperlib
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