In the context of AI, a parameterized model is one that depends on parameters which are adjusted through learning algorithms to better fit the data. These parameters are essentially the variables that the model uses to shape its output based on the input it receives. For example, in neural networks, the weights of the connections between neurons are parameters that are fine-tuned during training. The process involves iteratively adjusting these parameters to minimize the difference between the predicted output of the model and the actual output from the training data, thus improving the model's performance.
Historical Overview: The concept of parameterization has been foundational in statistics and computer science since the mid-20th century, but it gained significant attention in the field of AI with the rise of machine learning models in the 1980s and 1990s.
Key Contributors: While the concept of parameterization is a broad and fundamental idea in mathematics and science, in AI, notable contributions have come from the developers of early neural networks and machine learning algorithms, including but not limited to pioneers like Geoffrey Hinton and Yann LeCun, who worked extensively on parameterized models in the context of deep learning.