We stand with Ukraine

We stand with Ukraine

Ming Jin

Orcid: 0000-0002-6833-4811

Affiliations:

Monash University, Australia

According to our database¹, Ming Jin authored at least 34 papers between 2019 and 2024.

Collaborative distances:

Dijkstra number² of four.
Erdős number³ of four.

Timeline

Legend:

Book

In proceedings

Article

PhD thesis

Dataset

Other

Links

Online presence:

on orcid.org
on scholar.google.com

On csauthors.net:

Bibliography

2024

A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection.

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Geoffrey I. Webb

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IEEE Trans. Pattern Anal. Mach. Intell., December, 2024

Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects.

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IEEE Trans. Pattern Anal. Mach. Intell., October, 2024

Correlation-Aware Spatial-Temporal Graph Learning for Multivariate Time-Series Anomaly Detection.

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Yi-Ping Phoebe Chen

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IEEE Trans. Neural Networks Learn. Syst., September, 2024

Toward Graph Self-Supervised Learning With Contrastive Adjusted Zooming.

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IEEE Trans. Neural Networks Learn. Syst., July, 2024

Towards complex dynamic physics system simulation with graph neural ordinary equations.

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Neural Networks, 2024

Graph spatiotemporal process for multivariate time series anomaly detection with missing values.

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Yi-Ping Phoebe Chen

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Inf. Fusion, 2024

Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts.

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CoRR, 2024

Towards Universal Large-Scale Foundational Model for Natural Gas Demand Forecasting.

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CoRR, 2024

DyG-Mamba: Continuous State Space Modeling on Dynamic Graphs.

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CoRR, 2024

A Survey on Diffusion Models for Time Series and Spatio-Temporal Data.

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CoRR, 2024

Attractor Memory for Long-Term Time Series Forecasting: A Chaos Perspective.

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CoRR, 2024

Position Paper: What Can Large Language Models Tell Us about Time Series Analysis.

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CoRR, 2024

HiMTM: Hierarchical Multi-Scale Masked Time Series Modeling for Long-Term Forecasting.

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CoRR, 2024

Foundation Models for Time Series Analysis: A Tutorial and Survey.

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Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024

Position: What Can Large Language Models Tell Us about Time Series Analysis.

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Proceedings of the Forty-first International Conference on Machine Learning, 2024

Time-LLM: Time Series Forecasting by Reprogramming Large Language Models.

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Proceedings of the Twelfth International Conference on Learning Representations, 2024

HiMTM: Hierarchical Multi-Scale Masked Time Series Modeling with Self-Distillation for Long-Term Forecasting.

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Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, 2024

2023

Generative and Contrastive Self-Supervised Learning for Graph Anomaly Detection.

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Yi-Ping Phoebe Chen

IEEE Trans. Knowl. Data Eng., December, 2023

Multivariate Time Series Forecasting With Dynamic Graph Neural ODEs.

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IEEE Trans. Knowl. Data Eng., September, 2023

Graph Self-Supervised Learning: A Survey.

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IEEE Trans. Knowl. Data Eng., June, 2023

Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook.

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Vincent S. Tseng

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CoRR, 2023

Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects.

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CoRR, 2023

Towards Complex Dynamic Physics System Simulation with Graph Neural ODEs.

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CoRR, 2023

How Expressive are Spectral-Temporal Graph Neural Networks for Time Series Forecasting?

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CoRR, 2023

Geometric Relational Embeddings: A Survey.

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Mojtaba Nayyeri

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CoRR, 2023

2022

From Unsupervised to Few-shot Graph Anomaly Detection: A Multi-scale Contrastive Learning Approach.

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Yi-Ping Phoebe Chen

CoRR, 2022

Neural Temporal Walks: Motif-Aware Representation Learning on Continuous-Time Dynamic Graphs.

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Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

Towards Graph Self-Supervised Learning with Contrastive Adjusted Zooming.

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CoRR, 2021

Graph Self-Supervised Learning: A Survey.

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CoRR, 2021

Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning.

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Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021

ANEMONE: Graph Anomaly Detection with Multi-Scale Contrastive Learning.

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Proceedings of the CIKM '21: The 30th ACM International Conference on Information and Knowledge Management, Virtual Event, Queensland, Australia, November 1, 2021

2020

Optimized Coefficient Vector and Sparse Representation-Based Classification Method for Face Recognition.

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IEEE Access, 2020

Searching Correlated Patterns From Graph Streams.

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IEEE Access, 2020

2019

A Clickthrough Rate Prediction Algorithm Based on Users' Behaviors.

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IEEE Access, 2019

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