Spectral Filtering

Spectral filtering techniques are being actively developed to enhance various machine learning tasks by selectively modifying data representations in the frequency domain. Current research focuses on applications ranging from improving graph classification accuracy (using graph wavelet neural networks and spectral pooling) and sequential recommendation (via bidirectional state space models with FFT-based filtering) to mitigating biases in large language models (through spectral editing of activations) and enhancing the performance of graph neural networks (by employing spatially adaptive or node-oriented spectral filtering). These advancements demonstrate the broad utility of spectral filtering in addressing challenges related to data noise, long-range dependencies, and unwanted information, leading to improved model performance and robustness across diverse applications.