Tian Zhou
Orcid: 0000-0003-1789-5413Affiliations:
- Alibaba Group, DAMO Academy, Hangzhou, China
- Rutgers University, Department of Chemistry and Chemical Biology, New Brunswick, NJ, USA (PhD 2016)
According to our database1,
Tian Zhou
authored at least 30 papers
between 2017 and 2024.
Collaborative distances:
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Bibliography
2024
Mitigating Time Discretization Challenges with WeatherODE: A Sandwich Physics-Driven Neural ODE for Weather Forecasting.
CoRR, 2024
Less is more: Embracing sparsity and interpolation with Esiformer for time series forecasting.
CoRR, 2024
S<sup>3</sup>Attention: Improving Long Sequence Attention with Smoothed Skeleton Sketching.
CoRR, 2024
Sparse-VQ Transformer: An FFN-Free Framework with Vector Quantization for Enhanced Time Series Forecasting.
CoRR, 2024
FusionSF: Fuse Heterogeneous Modalities in a Vector Quantized Framework for Robust Solar Power Forecasting.
Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024
Proceedings of the Twelfth International Conference on Learning Representations, 2024
2023
Energy forecasting with robust, flexible, and explainable machine learning algorithms.
AI Mag., December, 2023
One Fits All: Universal Time Series Analysis by Pretrained LM and Specially Designed Adaptors.
CoRR, 2023
GCformer: An Efficient Framework for Accurate and Scalable Long-Term Multivariate Time Series Forecasting.
CoRR, 2023
Make Transformer Great Again for Time Series Forecasting: Channel Aligned Robust Dual Transformer.
CoRR, 2023
How Expressive are Spectral-Temporal Graph Neural Networks for Time Series Forecasting?
CoRR, 2023
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023
DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection.
Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023
Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023
SADI: A Self-Adaptive Decomposed Interpretable Framework for Electric Load Forecasting Under Extreme Events.
Proceedings of the IEEE International Conference on Acoustics, 2023
GCformer: An Efficient Solution for Accurate and Scalable Long-Term Multivariate Time Series Forecasting.
Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, 2023
eForecaster: Unifying Electricity Forecasting with Robust, Flexible, and Explainable Machine Learning Algorithms.
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023
2022
FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022
Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022
Learning to Rotate: Quaternion Transformer for Complicated Periodical Time Series Forecasting.
Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022
FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting.
Proceedings of the International Conference on Machine Learning, 2022
TFAD: A Decomposition Time Series Anomaly Detection Architecture with Time-Frequency Analysis.
Proceedings of the 31st ACM International Conference on Information & Knowledge Management, 2022
2017
Automatic segmentation of left ventricle in cardiac cine MRI images based on deep learning.
Proceedings of the Medical Imaging 2017: Image Processing, 2017
Transfer Learning for the Fully Automatic Segmentation of Left Ventricle Myocardium in Porcine Cardiac Cine MR Images.
Proceedings of the Statistical Atlases and Computational Models of the Heart. ACDC and MMWHS Challenges, 2017