Kähler landscapes for complex neural network descents and guarantees including a search and destroy of the Calabi-Yau manifold
We study landscapes for complex-parameterized networks. Our approach is motivated with an information-theoretic manifold perspective of the parameter and via classical optimization guarantees although of complex geometric variety such as through Dolbeault asymptotics. The descent path admits a Kähler information metric under a cross-entropy via the Wirtinger Hessian on the log-likelihood potential. We restrict attention to a descent update rule with natural gradient descent via a differentiated loss scaled by the inverse metric, so the descent path remains in the holomorphic tangent bundle. We emphasize Calabi-Yau information manifolds which profane theoretical guarantees via an ill-curva...
| Authors | Andrew Gracyk |
|---|---|
| Primary category | cs.LG |
| Published | 20 August 2026 |
| Last updated at source | 8 October 2026 |
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