CoCo-LoRA uses audio context to modulate uncertainty in Bayesian low-rank adapters for multimodal text tasks, offering a lightweight alternative to feature fusion that matches or exceeds baselines.
Fedpara: Low-rank hadamard product for communication-efficient federated learning
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A correlation-based taxonomy unifies existing FL compression methods, experiments show correlation strengths vary by task and architecture, and adaptive mode-switching designs are proposed to exploit this.
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Beyond Feature Fusion: Contextual Bayesian PEFT for Multimodal Uncertainty Estimation
CoCo-LoRA uses audio context to modulate uncertainty in Bayesian low-rank adapters for multimodal text tasks, offering a lightweight alternative to feature fusion that matches or exceeds baselines.
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Exploiting Correlations in Federated Learning: Opportunities and Practical Limitations
A correlation-based taxonomy unifies existing FL compression methods, experiments show correlation strengths vary by task and architecture, and adaptive mode-switching designs are proposed to exploit this.