Wave MLP

Wave-based machine learning models leverage the mathematical properties of waves to improve the representation and processing of data in various domains. Current research focuses on developing novel architectures, such as Wave-MLP and Wave-RNN, that incorporate wave-like representations to enhance feature extraction and learning efficiency, particularly in tasks involving sequential data or signals with inherent wave-like characteristics. These methods show promise in improving performance across diverse applications, including image analysis, geophysical modeling, and biomedical signal processing, by incorporating physical constraints or dynamically modulating feature interactions. The resulting improvements in accuracy and efficiency highlight the potential of wave-inspired approaches for advancing machine learning capabilities.