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cs.LG2026
SWAN: World-Aware Adaptive Multimodal Networks for Runtime Variations
Jason Wu, Shir-Kang Scott Jin, Yuyang Yuan +4
Multimodal deep neural networks deployed in realistic environments must contend with runtime variations: changes in modality quality, overall input complexity, and available platfo…
cs.LG2025
ADMN: A Layer-Wise Adaptive Multimodal Network for Dynamic Input Noise and Compute Resources
Jason Wu, Yuyang Yuan, Kang Yang +2
Multimodal deep learning systems are deployed in dynamic scenarios due to the robustness afforded by multiple sensing modalities. Nevertheless, they struggle with varying compute r…
cs.LG2024
MMBind: Unleashing the Potential of Distributed and Heterogeneous Data for Multimodal Learning in IoT
Xiaomin Ouyang, Jason Wu, Tomoyoshi Kimura +4
Multimodal sensing systems are increasingly prevalent in various real-world applications. Most existing multimodal learning approaches heavily rely on training with a large amount…