Neural Network Selection

Neural network selection focuses on efficiently choosing the best pre-trained model for a specific downstream task, avoiding the computationally expensive process of training numerous models from scratch. Current research explores various approaches, including statistical methods like Bayesian Information Criterion for model parsimony, analysis of network stability under graph perturbations (especially in graph neural networks), and adaptive regularization techniques that adjust model complexity based on input data difficulty. Effective neural network selection promises to significantly reduce computational costs and improve the efficiency of deep learning applications across diverse fields, from medical image analysis to natural language processing.