Model Complexity

Model complexity in machine learning focuses on understanding the relationship between a model's size, structure, and its performance, aiming to optimize for accuracy while minimizing resource consumption and improving interpretability. Current research investigates this relationship across diverse model architectures, including transformers, mixtures-of-experts, and various neural network types, employing techniques like feature engineering, model pruning, and novel complexity metrics beyond simple parameter counts. These efforts are crucial for advancing both theoretical understanding of generalization and practical applications, particularly in resource-constrained environments and safety-critical domains where model interpretability is paramount.