Layer Regression

Layer regression focuses on developing and analyzing methods for fitting complex functions using neural networks with multiple layers. Current research emphasizes the convergence properties of algorithms, particularly approximate Newton methods, applied to two-layer models employing activation functions like ReLU and softmax. This work aims to improve the accuracy and efficiency of function approximation, with applications ranging from mathematical function fitting to more complex tasks in areas like natural language processing. The development of robust and efficient multi-layer regression techniques holds significant potential for advancing various fields requiring accurate function approximation.