Anything-in-anything-out: a new modular AI model
Researchers at EPFL have developed a new, uniquely modular machine learning model for flexible decision-making
Creating MM models at a smaller scale poses significant challenges, including the problem of being robust to non-random missing information. This is information that a model doesn’t have, often due to some biased availability in resources. It is thus critical to ensure the model does not learn the patterns of biased missingness in making its predictions.
MultiModN turns this around
In response to this problem, researchers from the Machine Learning for Education (ML4ED) and Machine Learning and Optimization (MLO) Laboratories in EPFL’s School of Computer and Communication Sciences have developed and tested the exact opposite to a Large Language Model.
Spearheaded by Professor Mary-Anne Hartley, head of the Laboratory for intelligent Global Health Technologies hosted jointly in the MLO and the Yale School of Medicine and Professor Tanja Käser, head of ML4ED, MultiModN is a unique Modular Multimodal Model, presented recently at the NeurIPS2023 conference.
Like existing Multimodal Models MultiModN can learn from text, images, video, and sound. Unlike existing MMs, it is made up of any number of smaller, self-contained, and input-specific modules that can be selected depending on the information available, and then strung together in a sequence of any number, combination, or type of input. Itcan then output any number, or combination, of predictions.
“We evaluated MultiModN across ten real-world tasks including medical diagnosis support, academic performance prediction, and weather forecasting. Through these experiments, we believe that MultiModN is the first inherently interpretable, MNAR-resistant approach to multimodal modelling,” explained Vinitra Swamy, a PhD student with ML4ED and MLO and joint first author on the project.
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