Rich Input

Rich input research explores how the quality and quantity of data fed into machine learning models affect performance and robustness. Current efforts focus on mitigating challenges like occlusion in image processing, handling missing or uncertain inputs through techniques such as marginalization and input normalization, and improving model explainability with partially specified inputs. This research is crucial for advancing the reliability and efficiency of machine learning across diverse applications, from improving large language models to optimizing resource-intensive tasks like acoustic recognition and hypersonic flow simulations.