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Predicting multiple real-world tasks in a single model often requires aparticularly diverse feature space. Multimodal (MM) models aim to extract thesynergistic predictive potential of multiple data types to create a sharedfeature space with aligned semantic meaning across inputs of drasticallyvarying sizes (i.e. images, text, sound). Most current MM architectures fusethese representations in parallel, which not only limits their interpretabilitybut also creates a dependency on modality availability. We present MultiModN, amultimodal, modular network that fuses latent representations in a sequence ofany number, combination, or type of modality while providing granular real-timepredictive feedback on any number or combination of predictive tasks.MultiModN's composable pipeline is interpretable-by-design, as well as innatelymulti-task and robust to the fundamental issue of biased missingness. Weperform four experiments on several benchmark MM datasets across 10 real-worldtasks (predicting medical diagnoses, academic performance, and weather), andshow that MultiModN's sequential MM fusion does not compromise performancecompared with a baseline of parallel fusion. By simulating the challenging biasof missing not-at-random (MNAR), this work shows that, contrary to MultiModN,parallel fusion baselines erroneously learn MNAR and suffer catastrophicfailure when faced with different patterns of MNAR at inference. To the best ofour knowledge, this is the first inherently MNAR-resistant approach to MMmodeling. In conclusion, MultiModN provides granular insights, robustness, andflexibility without compromising performance.

MultiModN- Multimodal, Multi-Task, Interpretable Modular Networks