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This paper introduces a uniform-phase initialization method for neural networks with sine activations that eliminates reliance on the Central Limit Theorem, improving stability and performance in representation tasks like image and audio fitting.
This paper establishes high-probability bounds for mixed input derivatives of wide random tanh neural networks, demonstrating improved growth control with sufficient width and connections to quasi-Monte Carlo integration for training analysis.