Multi Modal Machine Learning

Multi-modal machine learning (MMML) focuses on integrating and analyzing data from diverse sources (e.g., images, text, sensor readings) to improve the accuracy and robustness of machine learning models. Current research emphasizes efficient inference serving techniques, such as dynamic modality selection, to address the computational demands of large multi-modal models, and explores various model architectures for joint representation learning across different data types. MMML's impact spans diverse fields, from improving medical diagnoses and risk assessments to optimizing engineering designs and enhancing gaming skill evaluations, demonstrating its broad applicability and potential for significant advancements across various disciplines.