arXiv Machine Learning
MuteBench: Modality Unavailability Tolerance Evaluation for Incomplete Multimodal Fusion
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
arXiv Machine Learning
Jun 5
PAMF: Prior-Aware Multimodal Fusion for Incomplete Time Series Data
arXiv:2606. 06328v1 Announce Type: new Abstract: In healthcare, multimodal time series tasks often operate on incomplete observations in practice, for example when ECG segments are lost because electrodes detach or an entire respiratory channel is unavailable during overnight monitoring.
arXiv Machine Learning
Jul 14
Multimodal Routing for Interpretable, Robust, and Auditable Clinical Prediction
arXiv:2607. 09982v1 Announce Type: new Abstract: Electronic health record (EHR) data are inherently multimodal, and leveraging multiple modalities can improve predictive performance.
arXiv Machine Learning
Jul 31
TIER-MoE: Trust-Informed Expert Routing via Conditional Modality Risk for Multimodal Fusion in Biomedical Classification
arXiv:2607. 27289v1 Announce Type: new Abstract: The promise of multimodal fusion lies in combining complementary sources of evidence, yet more evidence does not always yield a better prediction.
arXiv AI
Jul 28
Co-Learning for Missing Arbitrary Modalities in Multi-modal Classification
arXiv:2607. 24683v1 Announce Type: cross Abstract: Multi-modal classification leverages complementary information across diverse data sources to enhance predictive performance.
arXiv Machine Learning
Jun 16
Unsupervised Learning for Missing Modalities in Multimodal Learning
arXiv:2606. 15743v1 Announce Type: new Abstract: This paper addresses the missing-modality challenge in multi-modal learning by introducing Unsupervised Learning for Missing Modalities in Multi-Modal Learning (UL4M4), a flexible framework that imputes missing feature embeddings in a task-independent manner before supervised prediction.
arXiv Machine Learning
Aug 10
Conformal Fusion Under Missing Modalities
arXiv:2608. 07183v1 Announce Type: new Abstract: Multimodal fusion architectures typically assume all modalities are available at inference, yet sensor failures, acquisition variability, and cost constraints routinely produce incomplete observations.