S-GAI: Spectral Geometry-Aware Initialization for Sigmoidal MLPs -- From Dataset Geometry to Network Weights
arXiv:2606.28444v1 Announce Type: new Abstract: Classical universal approximation theorems establish the expressive power of sigmoidal multilayer perceptrons, but they do not prescribe how initial weights should encode the geometry of a data distribution. We propose S-GAI, a spectral geometry-aware initialization framework for one-hidden-layer sigmoidal MLPs. Starting from the constructive idea that sigmoid units can act as smooth half-space gates, we move from hand-specified planar geometry to class-wise spectral geometry estimated from image data. For each class, SVD provides a mean, princip
The continuous drive for more efficient and robust AI training, particularly concerning fundamental neural network components, necessitates ongoing research into initialization techniques.
Improved initialization methods for MLPs can lead to faster training times, better convergence, and potentially more stable and performant AI models, impacting a wide range of AI applications.
The proposed S-GAI framework offers a data-driven approach to initializing sigmoidal MLPs, moving beyond heuristic methods by directly encoding dataset geometry into network weights.
- · AI researchers and data scientists
- · Companies deploying MLPs in complex tasks
- · Deep learning framework developers
- · Practitioners relying solely on generic or random initialization for MLPs
More efficient and effective training of sigmoidal MLPs, especially for classification tasks.
Reduced computational resources and time required to achieve optimal performance from certain neural network architectures.
Potentially enables the development of more complex and higher-performing AI systems in resource-constrained environments.
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Read at arXiv cs.LG