Fractional Optimizers Meet Fractal Activation Functions: An Empirical Study of Multi-Scale Optimization in Neural Network
Exploring the convergence of fractional derivatives and fractal activations for robust neural network optimization.
Fractional optimization methods and fractal activation functions are two independent directions for improving neural network training. Fractional optimizers extend first-order optimization through fractional derivatives and memory effects, whereas fractal activations introduce mu...
ArXiv CS.LG
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The integration of fractional calculus and fractal geometry introduces a novel, multi-scale approach to overcoming local minima in complex loss landscapes. This research demonstrates that while these techniques are not a universal panacea, their strategic pairing can significantly enhance training stability and performance in highly perturbed neural network environments.
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