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| Main Author: | |
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| Format: | Recurso digital |
| Language: | English |
| Published: |
Zenodo
2026
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| Subjects: | |
| Online Access: | https://doi.org/10.5281/zenodo.19338035 |
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Table of Contents:
- <p>We propose shifting neural network optimization from the parameter space into a dual space of dimensionality. Our core mechanism is the M-matrix—an analytical per-layer Jacobian derived from forward-pass activations, yielding exact gradients without a global computational graph. In this dual formulation, every weight update is the exact optimum of a least-squares problem, solved via a matrix-free Conjugate Gradient (CG) method.<br><br>Code: <a title="Exact Dual-Space Solver" href="https://github.com/v-perfilev/exact-dual-space-solver" rel="noopener">GitHub</a></p>