arXiv:2606.12362v1 Announce Type: new Abstract: We study multimodal learning under missing modalities, with particular motivation from bioscience applications in which heterogeneous modalities are often only partially available when decisions need to be made. We propose Latent World Recovery (LWR), a framework built on two key ideas: (i) modality-specific embeddings from different modalities are aligned in a shared latent space, and (ii) a unified representation is constructed by fusing only the embeddings of the modalities that are actually available at both training and inference time. Rathe
The increasing complexity and resource demands of real-world AI applications, particularly in fields with inherently incomplete data like bioscience, necessitate robust solutions for multimodal learning with missing information.
This research addresses a fundamental challenge in applying multimodal AI to high-stakes domains, enabling more resilient and reliable AI systems where data scarcity or partial availability is common.
The proposed Latent World Recovery framework offers a new methodological approach for building AI models that can effectively learn and infer from incomplete multimodal datasets, potentially accelerating AI adoption in data-constrained environments.
- · Bioscience AI researchers
- · Healthcare AI developers
- · AI model robustness companies
- · Machine learning framework providers
- · AI systems heavily reliant on complete, perfectly aligned multimodal datasets
Improved performance and broader applicability of multimodal AI in fields with inherent data incompleteness, such as medical diagnostics or drug discovery.
Reduced data acquisition costs and increased efficiency for AI development in domains where collecting complete multimodal data is prohibitive.
Acceleration of personalized medicine and synthetic biology applications by enabling more effective analysis of heterogeneous and often incomplete patient or biological datasets.
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Read at arXiv cs.LG