MLP Like Model

MLP-like models are a class of neural networks built without convolutional or attention mechanisms, aiming to achieve comparable performance to more complex architectures while offering increased efficiency and reduced computational cost. Current research focuses on developing novel MLP architectures, such as those incorporating dynamic mixing, frequency awareness, or specialized embedding techniques for numerical features, to improve accuracy and generalization across diverse tasks including image recognition, 3D object reconstruction, and tabular data analysis. These efforts are significant because they explore alternative pathways to high-performance deep learning, potentially leading to more efficient and accessible models for various applications.