Temporal Regularization

Temporal regularization is a technique used to improve the performance of machine learning models by incorporating temporal information and enforcing consistency across time in sequential data. Current research focuses on applying this technique to various models, including tensor factorizations (like PARAFAC2), recurrent neural networks (like GRUs), and spiking neural networks, often employing optimization methods such as ADMM to achieve temporal smoothness and reduce prediction delays or catastrophic forgetting. This approach is significant because it enhances the accuracy and robustness of models dealing with time-evolving data in diverse fields, such as graph signal processing, time series forecasting, and video analysis, leading to improved predictions and more reliable interpretations.