A dynamic Bayesian nonparametric model for blind calibration of sensor networks. - Jielong Yang; Wee-Peng Tay; Xionghu Zhong (ICASSP, 2017)

Using Social Network Information in Bayesian Truth Discovery. - Jielong Yang; Junshan Wang; Wee Peng Tay (CoRR, 2018)

GraphFlow: A New Graph Convolutional Network Based on Parallel Flows. - Feng Ji; Jielong Yang; Wee Peng Tay (CoRR, 2019)

An Unsupervised Bayesian Neural Network for Truth Discovery. - Jielong Yang; Wee Peng Tay (CoRR, 2019)

Using Social Network Information in Community-Based Bayesian Truth Discovery. - Jielong Yang; Junshan Wang; Wee Peng Tay (IEEE Trans. Signal Inf. Process. over Networks, 2019)

GFCN: A New Graph Convolutional Network Based on Parallel Flows. - Feng Ji; Jielong Yang; Qiang Zhang; Wee Peng Tay (ICASSP, 2020)

Multiple Acoustic Source Localization in Microphone Array Networks. - Jielong Yang; Xionghu Zhong; Weiguang Chen; Wenwu Wang 001 (IEEE ACM Trans. Audio Speech Lang. Process., 2021)

Cross-Domain Representation Learning for Clothes Unfolding in Robot-Assisted Dressing. - Jinge Qie; Yixing Gao; Runyang Feng; Xin Wang 035; Jielong Yang; Esha Dasgupta; Hyung Jin Chang; Yi Chang 001 (ECCV Workshops (6), 2022)

Sigmoidally Preconditioned Off-policy Learning: a new exploration method for reinforcement learning. - Xing Chen; Dongcui Diao; Hechang Chen; Hengshuai Yao; Jielong Yang; Haiyin Piao; Zhixiao Sun; Bei Jiang; Yi Chang 001 (CoRR, 2022)

Learning the Network of Graphs for Graph Neural Networks. - Yixiang Shan; Jielong Yang; Xing Liu 009; Yixing Gao; Hechang Chen; Shuzhi Sam Ge (CoRR, 2022)

An Unsupervised Bayesian Neural Network for Truth Discovery in Social Networks. - Jielong Yang; Wee Peng Tay (IEEE Trans. Knowl. Data Eng., 2022)

Leveraging Label Non-Uniformity for Node Classification in Graph Neural Networks. - Feng Ji; See Hian Lee; Hanyang Meng; Kai Zhao 010; Jielong Yang; Wee Peng Tay (ICML, 2023)

Thermal Environment of Typical Industrial Parks over Jinjiang-Shishi, in southeast Fujian Province, China. - Jielong Yang; Yige Bai; Dongfang Huang; Feng Chen 022 (IoTAAI, 2023)

Distributional Signals for Node Classification in Graph Neural Networks. - Feng Ji; See Hian Lee; Kai Zhao 010; Wee Peng Tay; Jielong Yang (CoRR, 2023)

Distribution shift mitigation at test time with performance guarantees. - Rui Ding; Jielong Yang; Feng Ji; Xionghu Zhong; Linbo Xie (CoRR, 2023)

Dual-decoder transformer network for answer grounding in visual question answering. - Liangjun Zhu; Li Peng; Weinan Zhou; Jielong Yang (Pattern Recognit. Lett., 2023)

GLAE: A graph-learnable auto-encoder for single-cell RNA-seq analysis. - Yixiang Shan; Jielong Yang; Xiangtao Li; Xionghu Zhong; Yi Chang 001 (Inf. Sci., 2023)

A novel relation aware wrapper method for feature selection. - Zhaogeng Liu; Jielong Yang; Li Wang; Yi Chang 001 (Pattern Recognit., 2023)

Towards fidelity of graph data augmentation via equivariance. - Bai Zhang; Yixing Gao; Feng Ji; Linbo Xie; Xiaofeng Cao 002; Yixiang Shan; Jielong Yang (Knowl. Based Syst., 2023)

An Attention Model Based Approach for Leakage Detection in Water Distribution Networks Using Normal Pressure Data. - Juan Luo; Du Zhou; Chongxiao Wang; Jielong Yang; Xionghu Zhong (PRICAI (5), 2024)

Control the GNN: Utilizing Neural Controller with Lyapunov Stability for Test-Time Feature Reconstruction. - Jielong Yang; Rui Ding; Feng Ji; Hongbin Wang; Linbo Xie (CoRR, 2024)

Dynamic spatial-temporal graph convolutional recurrent networks for traffic flow forecasting. - Zhichao Xia; Yong Zhang; Jielong Yang; Linbo Xie (Expert Syst. Appl., 2024)

GL-GNN: Graph learning via the network of graphs. - Yixiang Shan; Jielong Yang; Yixing Gao (Knowl. Based Syst., 2024)

Robust multi-agent reinforcement learning via Bayesian distributional value estimation. - Xinqi Du; Hechang Chen; Che Wang; Yongheng Xing; Jielong Yang; Philip S. Yu; Yi Chang 001; Lifang He 001 (Pattern Recognit., 2024)

Black-Box Attacks on Graph Neural Networks via White-Box Methods With Performance Guarantees. - Jielong Yang; Rui Ding 013; Jianyu Chen; Xionghu Zhong; Huarong Zhao; Linbo Xie (IEEE Internet Things J., 2024)

FR-GNN: Mitigating the Impact of Distribution Shift on Graph Neural Networks via Test-Time Feature Reconstruction. - Rui Ding 013; Jielong Yang; Feng Ji; Xionghu Zhong; Linbo Xie (IEEE Internet Things J., 2024)

Refining Euclidean Obfuscatory Nodes Helps: A Joint-Space Graph Learning Method for Graph Neural Networks. - Zhaogeng Liu; Feng Ji; Jielong Yang; Xiaofeng Cao 002; Muhan Zhang; Hechang Chen; Yi Chang 001 (IEEE Trans. Neural Networks Learn. Syst., 2024)

A Transformer-Based Approach to Leakage Detection in Water Distribution Networks. - Juan Luo; Chongxiao Wang; Jielong Yang; Xionghu Zhong (Sensors, 2024)

A Novel Composite Graph Neural Network. - Zhaogeng Liu; Jielong Yang; Xionghu Zhong; Wenwu Wang 001; Hechang Chen; Yi Chang 001 (IEEE Trans. Neural Networks Learn. Syst., 2024)

A Joint Time-Frequency Attention for Leakage Detection in Water Distribution Networks Using Time Series Decomposition. - Juan Luo; Yiyang Chen; Jielong Yang; Xionghu Zhong (ICASSP, 2025)

Rethinking Graph Neural Networks From A Geometric Perspective Of Node Features. - Feng Ji; Yanan Zhao 003; Kai Zhao 010; Hanyang Meng; Jielong Yang; Wee Peng Tay (ICLR, 2025)

Mitigating Over-Smoothing in Graph Neural Networks via Separation Coefficient-Guided Adaptive Graph Structure Adjustment. - Hanyang Meng; Jielong Yang; Li Peng (IJCAI, 2025)

UniArray: Unified Spectral-Spatial Modeling for Array-Geometry-Agnostic Speech Separation. - Weiguang Chen; Junjie Zhang; Jielong Yang; Eng Siong Chng; Xionghu Zhong (IEEE Signal Process. Lett., 2025)

Graph Distributional Signals for Regularization in Graph Neural Networks. - Feng Ji; Yanan Zhao 003; See Hian Lee; Kai Zhao 010; Wee Peng Tay; Jielong Yang (IEEE Trans. Signal Inf. Process. over Networks, 2025)

Towards a Reward-Free Reinforcement Learning Framework for Vehicle Control. - Jielong Yang; Daoyuan Huang (CoRR, 2025)

Resource-Efficient Motion Control for Video Generation via Dynamic Mask Guidance. - Sicong Feng; Jielong Yang; Li Peng (CoRR, 2025)

Semantically-Guided Inference for Conditional Diffusion Models: Enhancing Covariate Consistency in Time Series Forecasting. - Rui Ding; Hanyang Meng; Zeyang Zhang; Jielong Yang (CoRR, 2025)