AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This paper proposes multimodal Max Confidence Regularization (MaxCR) to dynamically intervene in modality semantic confidence, and reveals that this flaw stems from unimodal characteristics rather than multimodal learning, and this confidence discrepancy can be corrected by positive cross-modal intervention.
This paper develops a dynamic strategy that jointly optimizes modality fusion and alignment, and develops a learning-based strategy using a bi-level optimization framework and theoretically proves the convergence of the learning algorithm to ensure its reliability.
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