9 citations · 27 across the 12 of their papers we have counts for
3 papers · 2 filters
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…
Perturbation-based Graph Active Learning for Weakly-Supervised Belief Representation Learning
Dachun Sun, Ruijie Wang, Jinning Li +4
This paper addresses the problem of optimizing the allocation of labeling resources for semi-supervised belief representation learning in social networks. The objective is to strat…
SudokuSens: Enhancing Deep Learning Robustness for IoT Sensing Applications using a Generative Approach
Tianshi Wang, Jinyang Li, Ruijie Wang +7
This paper introduces SudokuSens, a generative framework for automated generation of training data in machine-learning-based Internet-of-Things (IoT) applications, such that the ge…