56 citations · 140 across the 14 of their papers we have counts for
7 papers · 1 filter
Foundation Models for CPS-IoT: Opportunities and Challenges
Ozan Baris, Yizhuo Chen, Gaofeng Dong +9
Methods from machine learning (ML) have transformed the implementation of Perception-Cognition-Communication-Action loops in Cyber-Physical Systems (CPS) and the Internet of Things…
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…
On the Efficiency and Robustness of Vibration-based Foundation Models for IoT Sensing: A Case Study
Tomoyoshi Kimura, Jinyang Li, Tianshi Wang +9
This paper demonstrates the potential of vibration-based Foundation Models (FMs), pre-trained with unlabeled sensing data, to improve the robustness of run-time inference in (a cla…
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…
Noisy Positive-Unlabeled Learning with Self-Training for Speculative Knowledge Graph Reasoning
Ruijie Wang, Baoyu Li, Yichen Lu +6
This paper studies speculative reasoning task on real-world knowledge graphs (KG) that contain both \textit{false negative issue} (i.e., potential true facts being excluded) and \t…
Learning to Sample and Aggregate: Few-shot Reasoning over Temporal Knowledge Graphs
Ruijie Wang, Zheng Li, Dachun Sun +4
In this paper, we investigate a realistic but underexplored problem, called few-shot temporal knowledge graph reasoning, that aims to predict future facts for newly emerging entiti…
Revisiting Over-smoothing in Deep GCNs
Chaoqi Yang, Ruijie Wang, Shuochao Yao +2
Oversmoothing has been assumed to be the major cause of performance drop in deep graph convolutional networks (GCNs). In this paper, we propose a new view that deep GCNs can actual…