# A Modular End-to-End Multimodal Learning Method for Structured and Unstructured Data

arXiv: https://arxiv.org/abs/2403.04866 · HTML: https://mlsanity.com/paper/2403.04866/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- First posted on arXiv 7 Mar 2024.

## Abstract (arXiv)

Multimodal learning is a rapidly growing research field that has revolutionized multitasking and generative modeling in AI. While much of the research has focused on dealing with unstructured data (e.g., language, images, audio, or video), structured data (e.g., tabular data, time series, or signals) has received less attention. However, many industry-relevant use cases involve or can be benefited from both types of data. In this work, we propose a modular, end-to-end multimodal learning method called MAGNUM, which can natively handle both structured and unstructured data. MAGNUM is flexible enough to employ any specialized unimodal module to extract, compress, and fuse information from all available modalities.