What is the Best Way to Train AI Models?

1 Mar 2025

Fine-tuning outperforms full-training in Hi-Mapper’s visual hierarchy learning, optimizing feature representation in CNNs and transformer models.

Supplementary Materials for Study on Hyperbolic Visual Hierarchy Mapping

1 Mar 2025

Supplementary details on Hyperbolic Visual Hierarchy Mapping, including network architecture, theoretical baseline, and additional experimental results.

What If AI Understood Images Like We Do? This Model Might

1 Mar 2025

Hi-Mapper enhances AI’s ability to understand visual hierarchies using hyperbolic space learning, improving scene comprehension and deep learning models.

What AI ‘Sees’ and Why It Matters

1 Mar 2025

Discover how hyperbolic space, probabilistic modeling, and hierarchy structure affect AI recognition accuracy.

Teaching AI to See the World Like Humans

27 Feb 2025

See how Hi-Mapper enhances AI vision tasks, outperforming baseline models in image classification, object detection, and segmentation.

Why Hyperbolic Space Matters for AI Scene Recognition

27 Feb 2025

Hi-Mapper improves AI scene recognition by mapping visual hierarchies in hyperbolic space, enhancing deep learning models with structured hierarchical encoding.

How AI Models Understand Visual Hierarchies

27 Feb 2025

This study explores hierarchy-aware deep learning, using hyperbolic geometry and probabilistic modeling to enhance AI’s understanding of visual structures.

This New Algorithm Puts AI’s Vision to the Test—Literally

27 Feb 2025

Learn how Hi-Mapper enhances AI’s scene recognition by mapping visual hierarchies in hyperbolic space.

Mathematical Proofs for SPD Inner Products and Pseudo-Gyrodistances in Manifold Layers

4 Dec 2024

Detailed proofs for SPD spaces, inner products, pseudo-gyrodistances, and FC layers, referencing Nguyen & Yang (2023) and Pennec et al. (2020).