Stationary Representation

Stationary representations aim to create data representations that remain consistent and comparable across different models or datasets, enabling seamless model updates and interoperability. Current research focuses on developing algorithms and model architectures, such as those based on transformers, normalizing flows, and contrastive learning, to achieve this stability, often addressing challenges like class imbalance, missing data, and non-stationarity in time series. This work has significant implications for various fields, improving the efficiency and robustness of applications ranging from personalized education and emotion recognition to search systems and continuous learning scenarios. The development of stable, information-rich representations is crucial for advancing machine learning and data analysis across diverse domains.