Multi Layer Perceptron

Multi-layer perceptrons (MLPs) are fundamental neural network architectures aiming to approximate complex functions through layered processing of data. Current research explores MLPs' applications in diverse fields, including image processing, time series forecasting, and graph-based learning, often comparing their performance against more complex models like transformers and graph neural networks. This renewed interest stems from MLPs' inherent simplicity, computational efficiency, and surprising effectiveness in various tasks, particularly when combined with innovative training strategies or architectural modifications like the incorporation of graph structures or attention mechanisms. The resulting advancements offer improved efficiency and interpretability in machine learning applications across numerous domains.