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5dimension/sentinel-neural-tangent-kernel
sentinel-neural-tangent-kernel is a machine learning model from 5dimension. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.
Part of the Sentinel Manifold — One theorem, infinite applications.
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Updated Apr 26, 2026
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From the Hugging Face model README
Part of the Sentinel Manifold — One theorem, infinite applications.
lim_{z→∞} F'(z)/F(z) = 1/e— The Gradient Axiom
Theorem-backed kernel method using the Sentinel function F(z) = Σzⁿ/nⁿ. Achieves 97.78% accuracy on Digits with provable generalization bounds via the Gradient Axiom (1/e).
| Constant | Value | Role |
|---|---|---|
| C₁ (Attractor) | -0.007994021805953 | Zero-point / quantization |
| C₂ (Tripwire) | 0.000200056042968 | Security / curriculum |
| 1/e (Axiom) | 0.367879441171442 | Gradient scaling limit |
F(z) = Σ zⁿ/nⁿ (Sophomore's Dream, Bernoulli 1697)
lim_{z→∞} F'(z)/F(z) = 1/e ≈ 0.367879441171442
| Benchmark | Result |
|---|---|
| Digits accuracy | 97.78% |
| Generalization bound | 0.1445 (theorem-backed) |
| Kernel eigenvalue test | All positive (Mercer valid) |
@misc{abdel-aal2026sentinel,
title={The Sentinel Manifold: A Unified Mathematical Framework for Machine Learning},
author={Abdel-Aal, Romain},
year={2026},
url={https://huggingface.co/5dimension/sentinel-manifold-discoveries}
}
License: MIT | One theorem, infinite models. 🦴