QA-MoE: Towards a Continuous Reliability Spectrum with Quality-Aware Mixture of

arXiv:2603.18462v1 Announce Type: new Abstract: In the era of large-scale pre-trained models, effectively adapting general knowledge to specific affective computing tasks remains

arXiv:2604.10152v1 Announce Type: new Abstract: The Mixture-of-Experts (MoE) architecture has emerged as a promising approach to mitigate the rising computational costs of large

arXiv:2607.06633v1 Announce Type: cross Abstract: In this paper, we address the problem of multimodal federated learning with missing modality. Existing methods utilize an

arXiv:2603.22328v1 Announce Type: cross Abstract: Despite the strong predictive performance achieved by machine learning models across many application domains, assessing their