Ming Jin
Ming Jin
Assistant Professor at School of ICT, Griffith University
Verified email at - Homepage
Cited by
Cited by
Graph Self-Supervised Learning: A Survey
Y Liu, M Jin, S Pan, C Zhou, F Xia, PS Yu
IEEE Transactions on Knowledge and Data Engineering, 2022
Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning
M Jin, Y Zheng, YF Li, C Gong, C Zhou, S Pan
International Joint Conference on Artificial Intelligence (IJCAI), 2021
Time-LLM: Time Series Forecasting by Reprogramming Large Language Models
M Jin, S Wang, L Ma, Z Chu, JY Zhang, X Shi, PY Chen, Y Liang, YF Li, ...
International Conference on Learning Representations (ICLR), 2024
Generative and Contrastive Self-Supervised Learning for Graph Anomaly Detection
Y Zheng, M Jin, Y Liu, L Chi, KT Phan, YPP Chen
IEEE Transactions on Knowledge and Data Engineering, 2021
ANEMONE: Graph Anomaly Detection with Multi-Scale Contrastive Learning
M Jin, Y Liu, Y Zheng, L Chi, YF Li, S Pan
Proceedings of the 30th ACM International Conference on Information …, 2021
A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection
M Jin, HY Koh, Q Wen, D Zambon, C Alippi, GI Webb, I King, S Pan
arXiv preprint arXiv:2307.03759, 2023
Multivariate Time Series Forecasting with Dynamic Graph Neural ODEs
M Jin, Y Zheng, YF Li, S Chen, B Yang, S Pan
IEEE Transactions on Knowledge and Data Engineering, 2022
Neural Temporal Walks: Motif-Aware Representation Learning on Continuous-Time Dynamic Graphs
M Jin, YF Li, S Pan
Advances in Neural Information Processing Systems (NeurIPS), 2022
Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects
K Zhang, Q Wen, C Zhang, R Cai, M Jin, Y Liu, J Zhang, Y Liang, G Pang, ...
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024
Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook
M Jin, Q Wen, Y Liang, C Zhang, S Xue, X Wang, J Zhang, Y Wang, ...
arXiv preprint arXiv:2310.10196, 2023
Optimized Coefficient Vector and Sparse Representation-Based Classification Method for Face Recognition
S Liu, L Li, M Jin, S Hou, Y Peng
IEEE Access, 2019
Towards Graph Self-Supervised Learning with Contrastive Adjusted Zooming
Y Zheng, M Jin, S Pan, YF Li, H Peng, M Li, Z Li
IEEE Transactions on Neural Networks and Learning Systems, 2021
From Unsupervised to Few-shot Graph Anomaly Detection: A Multi-scale Contrastive Learning Approach
Y Zheng, M Jin, Y Liu, L Chi, KT Phan, S Pan, YPP Chen
arXiv preprint arXiv:2202.05525, 2022
Foundation models for time series analysis: A tutorial and survey
Y Liang, H Wen, Y Nie, Y Jiang, M Jin, D Song, S Pan, Q Wen
Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and …, 2024
What Can Large Language Models Tell Us about Time Series Analysis
M Jin, Y Zhang, W Chen, K Zhang, Y Liang, B Yang, J Wang, S Pan, ...
International Conference on Machine Learning (ICML), 2024
Correlation-aware Spatial-Temporal Graph Learning for Multivariate Time-series Anomaly Detection
Y Zheng, HY Koh, M Jin, L Chi, KT Phan, S Pan, YPP Chen, W Xiang
IEEE Transactions on Neural Networks and Learning Systems, 2023
WeaverBird: Empowering Financial Decision-Making with Large Language Model, Knowledge Base, and Search Engine
S Xue, F Zhou, Y Xu, M Jin, Q Wen, H Hao, Q Dai, C Jiang, H Zhao, S Xie, ...
arXiv preprint arXiv:2308.05361, 2023
Searching Correlated Patterns From Graph Streams
M Jin, M Li, Y Zheng, L Chi
IEEE Access, 2020
A Clickthrough Rate Prediction Algorithm Based on Users’ Behaviors
X Xiong, C Xie, R Zhao, Y Li, S Ju, M Jin
IEEE Access, 2019
How Expressive are Spectral-Temporal Graph Neural Networks for Time Series Forecasting?
M Jin, G Shi, YF Li, Q Wen, B Xiong, T Zhou, S Pan
arXiv preprint arXiv:2305.06587, 2023
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