Incremental Data

Incremental data processing focuses on efficiently updating machine learning models as new data arrives sequentially, avoiding retraining from scratch and mitigating catastrophic forgetting. Current research emphasizes developing algorithms that enable warm-starting, incorporating knowledge distillation and feature regularization techniques, and handling various data modalities (e.g., time series, graphs, tabular data) with architectures like neural operators and transformers. This field is crucial for real-world applications involving continuous data streams, improving model efficiency and adaptability in domains such as autonomous driving, federated learning, and recommender systems.