Method and Apparatus for Relativistic Manifold Embedding and Normalization in Deep Neural Networks

This technical report specifies the mathematical formulation and architectural design for Relativistic Manifold Embedding and Normalization in deep neural networks. It introduces Minkowski RMSNorm, dynamic hyperboloid coordinate projection mechanics, analytical timelike coordinate derivation, and a hybrid exact/asymptotic logarithmic geodesic distance matrix calculation algorithm on a Pseudo-Riemannian Lorentz Hyperboloid manifold. ---Author Links & Resources:- Personal Website: https://baar.uk/- Hugging Face Profile: https://huggingface.co/aixk

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-08-03
DOI
https://doi.org/10.5281/zenodo.21775576
Primary Topic
Model Reduction and Neural Networks
Type
article
Field-Weighted Citation Impact
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article

Method and Apparatus for Relativistic Manifold Embedding and Normalization in Deep Neural Networks

Youngjin Oh
Zenodo (CERN European Organization for Nuclear Research)
Model Reduction and Neural Networks
article

Method and Apparatus for Relativistic Manifold Embedding and Normalization in Deep Neural Networks

Youngjin Oh
article en

Abstract

This technical report specifies the mathematical formulation and architectural design for Relativistic Manifold Embedding and Normalization in deep neural networks. It introduces Minkowski RMSNorm, dynamic hyperboloid coordinate projection mechanics, analytical timelike coordinate derivation, and a hybrid exact/asymptotic logarithmic geodesic distance matrix calculation algorithm on a Pseudo-Riemannian Lorentz Hyperboloid manifold. ---Author Links & Resources:- Personal Website: https://baar.uk/- Hugging Face Profile: https://huggingface.co/aixk

Zenodo (CERN European Organization for Nuclear Research)
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Model Reduction and Neural Networks
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Method and Apparatus for Relativistic Manifold Embedding and Normalization in Deep Neural Networks — Youngjin Oh · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS