Multi Layer

Multi-layer architectures are a central theme in contemporary machine learning, aiming to improve the efficiency and accuracy of various models by strategically organizing computational units into multiple layers. Current research focuses on optimizing these architectures, exploring alternatives to traditional multilayer perceptrons (MLPs) such as Kolmogorov-Arnold Networks (KANs) and Fourier Analysis Networks (FANs), and investigating techniques like layer distillation and frequency shifting for improved performance and reduced computational cost. These advancements have significant implications for diverse applications, including music generation, image processing, natural language processing, and scientific computing, by enabling faster, more accurate, and more efficient models.