no code implementations • 23 Feb 2024 • Kun Yi, Qi Zhang, Hui He, Kaize Shi, Liang Hu, Ning An, Zhendong Niu
Multivariate time series (MTS) forecasting is crucial in many real-world applications.
no code implementations • 9 Jan 2024 • Yuanchi Ma, Hui He, Zhongxiang Lei, Zhendong Niu
However, for existing graph autoencoder clustering algorithms based on GCN or GAT, not only do they lack good generalization ability, but also the number of clusters clustered by such autoencoder models is difficult to determine automatically.
1 code implementation • 13 Dec 2023 • Xiaojun Xue, Chunxia Zhang, Tianxiang Xu, Zhendong Niu
However, the present few-shot NER models assume that the labeled data are all clean without noise or outliers, and there are few works focusing on the robustness of the cross-domain transfer learning ability to textual adversarial attacks in Few-shot NER.
1 code implementation • NeurIPS 2023 • Kun Yi, Qi Zhang, Wei Fan, Shoujin Wang, Pengyang Wang, Hui He, Defu Lian, Ning An, Longbing Cao, Zhendong Niu
FreTS mainly involves two stages, (i) Domain Conversion, that transforms time-domain signals into complex numbers of frequency domain; (ii) Frequency Learning, that performs our redesigned MLPs for the learning of real and imaginary part of frequency components.
no code implementations • 4 Feb 2023 • Kun Yi, Qi Zhang, Longbing Cao, Shoujin Wang, Guodong Long, Liang Hu, Hui He, Zhendong Niu, Wei Fan, Hui Xiong
Despite the growing attention and the proliferation of research in this emerging field, there is currently a lack of a systematic review and in-depth analysis of deep learning-based time series models with FT.
1 code implementation • 27 Jan 2023 • Hui He, Qi Zhang, Shoujin Wang, Kun Yi, Zhendong Niu, Longbing Cao
To bridge such significant gap, we formulate the fairness modeling problem as learning informative representations attending to both advantaged and disadvantaged variables.
no code implementations • 6 Oct 2022 • Kun Yi, Qi Zhang, Liang Hu, Hui He, Ning An, Longbing Cao, Zhendong Niu
The key problem in multivariate time series (MTS) analysis and forecasting aims to disclose the underlying couplings between variables that drive the co-movements.
no code implementations • 1 Sep 2022 • Hui He, Qi Zhang, Kun Yi, Kaize Shi, Zhendong Niu, Longbing Cao
Most existing MTS forecasting models greatly suffer from distribution drift and degrade the forecasting performance over time.
no code implementations • International Joint Conferences on Artifical Intelligence (IJCAI) 2019 • Botian Shi, Lei Ji, Pan Lu, Zhendong Niu, Nan Duan
In this paper, we develop a Scene Concept Graph (SCG) by aggregating image scene graphs and extracting frequently co-occurred concept pairs as scene common-sense knowledge.
no code implementations • ACL 2019 • Botian Shi, Lei Ji, Yaobo Liang, Nan Duan, Peng Chen, Zhendong Niu, Ming Zhou
Understanding narrated instructional videos is important for both research and real-world web applications.