Upper Bounds for Local Learning Coefficients of Three-Layer Neural Networks
Researchers have developed a new formula to calculate an upper bound for local learning coefficients in three-layer neural networks. This formula addresses singular points, which were a limitation in previous methods. The new approach offers a counting rule based on budget, demand, and supply constraints and extends to a broader range of activation functions, including swish and polynomial types under specific conditions. AI
IMPACT Provides a new theoretical framework for understanding the learning behavior of specific neural network architectures.