MultiModN

MultiModN is an interpretable, modular network (NeurIPS 2023) that sequentially fuses any combination of modalities for multi-task prediction, offering inherent robustness against Missing Not-At-Random (MNAR) data bias.

MultiModN (Multimodal, Multi-Task, Interpretable Modular Networks) is your next-gen architecture for complex AI tasks. This modular network sequentially fuses latent representations from any number or type of modality, providing granular, real-time predictive feedback for multiple tasks. Unlike parallel fusion baselines, MultiModN is robust to the critical issue of Missing Not-At-Random (MNAR) data: a first-of-its-kind, inherently MNAR-resistant approach. It matches performance benchmarks across 10 real-world tasks (e.g., medical diagnoses, academic performance) while remaining interpretable-by-design and fully composable at inference.

https://github.com/EPFLiGHT/MultiModN