Multi Source

Multi-source learning focuses on leveraging information from multiple datasets to improve model performance on a target task, addressing limitations of single-source training, particularly in data-scarce scenarios. Current research emphasizes efficient methods for selecting and weighting multiple sources, employing techniques like meta-learning, ensemble methods, and attention mechanisms within architectures such as graph neural networks and transformer-based models. This approach is proving valuable across diverse fields, enhancing accuracy and robustness in applications ranging from time series forecasting and text classification to medical image analysis and financial risk prediction.