Parametric Learning

Parametric learning focuses on using parameterized models, often neural networks, to learn relationships within data and solve various problems, primarily by optimizing model parameters to minimize error. Current research emphasizes developing efficient and robust parametric models for diverse tasks, including temporal modeling (e.g., using piecewise linear networks), generalized category discovery (e.g., employing information maximization techniques), and solving partial differential equations (e.g., with Fourier Neural Operators and physics-informed neural networks). These advancements improve accuracy and efficiency in applications ranging from image processing and natural language processing to engineering simulations and biological modeling, offering a powerful tool for data analysis and scientific discovery.