Evaluates robust multimodal sensor fusion techniques, suggesting improved performance across diverse data types.
• Rigorous analysis proving the marginal convexity for multimodal autoencoders. • Block-wise optimization strategy for multimodal architectures. • Novel Lipschitz-based adaptive learning rate for scale mismatch. • Evaluation on real-world heterogeneous multimodal datasets.
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Altinses et al. (2026) studied this question.
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