VoodooNet Introduces Non-Iterative Neural Architecture with Galactic Expansion for AI Efficiency
VoodooNet achieves 98.10% accuracy on MNIST and 86.63% on Fashion-MNIST through a novel non-iterative neural architecture that eliminates stochastic gradient descent. By projecting input manifolds into a high-dimensional Galactic space, complex features are untangled without backpropagation's thermodynamic cost. The system uses Moore-Penrose pseudoinverse to solve for output layers in a single step, reducing training time by orders of magnitude compared to traditional methods. Performance on Fashion-MNIST surpasses a 10-epoch SGD baseline of 84.41%, demonstrating superior efficiency. A near-logarithmic scaling law reveals accuracy depends on Galactic volume rather than iterative refinement. This Magic Hat approach represents a new frontier in neural network design, published as arXiv:2604.15613v1 with cross-announcement type. The architecture's closed-form analytic solution via Galactic Expansion replaces conventional training paradigms entirely.
Key facts
- VoodooNet achieves 98.10% accuracy on MNIST
- VoodooNet achieves 86.63% accuracy on Fashion-MNIST
- Performance surpasses 10-epoch SGD baseline of 84.41% on Fashion-MNIST
- Uses non-iterative neural architecture eliminating stochastic gradient descent
- Projects input manifolds into high-dimensional Galactic space
- Employs Moore-Penrose pseudoinverse for single-step output layer solution
- Reduces training time by orders of magnitude
- Shows near-logarithmic scaling between dimensionality and accuracy
Entities
—