Interpretable Fusion

Interpretable fusion aims to combine information from multiple data sources (e.g., text, images, sensor data) while simultaneously providing insights into how the combined information leads to predictions. Current research focuses on developing methods that not only improve prediction accuracy but also offer explanations for the model's decisions, often employing techniques like tensor fusion, latent variable models (e.g., Gaussian Processes), and self-attention mechanisms to achieve this interpretability. This field is significant because it addresses the "black box" nature of many machine learning models, enhancing trust and facilitating human-machine collaboration in diverse applications ranging from medical diagnosis to swarm robotics.