Multimodal Regression

Multimodal regression aims to improve prediction accuracy and robustness by integrating data from multiple sources (e.g., images, audio, sensor readings). Current research focuses on developing sophisticated models, including deep ensembles, recurrent neural networks, and novel radial basis function networks, to effectively fuse diverse data types while managing uncertainty and addressing challenges like data sparsity and modality-specific noise. These advancements are impacting various fields, enabling more accurate predictions in applications ranging from traffic analysis and environmental monitoring to medical imaging and speech processing. The emphasis is on developing trustworthy methods that provide not only predictions but also reliable uncertainty estimates.