Tunable parameters, also known as hyperparameters, are critical in defining the structure and behavior of AI models, particularly in machine learning. These parameters, which are set before the training process begins, control various aspects of the model such as learning rate, number of layers, number of neurons per layer, batch size, and regularization techniques. Unlike model parameters that are learned directly from the training data, tunable parameters are set based on trial and error, often using techniques like grid search or randomized search to find the most effective values. Optimal tuning of these parameters can significantly affect the model's accuracy, efficiency, and ability to generalize from training data to unseen data.
Historical overview: The concept of tunable parameters has been intrinsic to machine learning since its early days, but it gained distinct recognition and critical importance with the rise of more complex models like neural networks in the 1980s and 1990s. Methods for optimizing these parameters have evolved, becoming a central focus of research in the field.
Key contributors: There are no single contributors to the concept of tunable parameters as it is a fundamental aspect of machine learning involving contributions from numerous researchers across the discipline. However, the development of automated hyperparameter optimization techniques has seen significant contributions from researchers like Frank Hutter, Cedric Archambeau, and James Bergstra, among others.