2 papers
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…
cs.IT2024
Distributed and Rate-Adaptive Feature Compression
Aditya Deshmukh, Venugopal V. Veeravalli, Gunjan Verma
We study the problem of distributed and rate-adaptive feature compression for linear regression. A set of distributed sensors collect disjoint features of regressor data. A fusion…