1 code implementation • International Conference on Learning Representations 2024 • Hangting Ye, Wei Fan, Xiaozhuang Song, Shun Zheng, He Zhao, Dan dan Guo, Yi Chang
With the recent success of deep learning, many tabular machine learning (ML) methods based on deep networks (e. g., Transformer, ResNet) have achieved competitive performance on tabular benchmarks.
no code implementations • 30 Jan 2024 • Wei Fan, Shun Zheng, Pengyang Wang, Rui Xie, Jiang Bian, Yanjie Fu
Due to non-stationarity of time series, the distribution shift problem largely hinders the performance of time series forecasting.
1 code implementation • 23 Oct 2023 • Han Zhang, Xiaofan Gui, Shun Zheng, Ziheng Lu, Yuqi Li, Jiang Bian
Battery degradation remains a pivotal concern in the energy storage domain, with machine learning emerging as a potent tool to drive forward insights and solutions.
no code implementations • 11 Oct 2023 • Jiawen Zhang, Xumeng Wen, Shun Zheng, Jia Li, Jiang Bian
Deep time-series forecasting plays an integral role in numerous practical applications.
no code implementations • 11 Oct 2023 • Han Zhang, Xumeng Wen, Shun Zheng, Wei Xu, Jiang Bian
Despite considerable efforts in developing effective learning models for tabular data, current transferable tabular models remain in their infancy, limited by either the lack of support for direct instruction following in new tasks or the neglect of acquiring foundational knowledge and capabilities from diverse tabular datasets.
no code implementations • 8 Oct 2023 • Han Zhang, Yuqi Li, Shun Zheng, Ziheng Lu, Xiaofan Gui, Wei Xu, Jiang Bian
Here we introduce a universal deep learning approach that is capable of accommodating various aging conditions and facilitating effective learning under low-resource conditions by leveraging data from rich conditions.
1 code implementation • 14 Jun 2023 • Jiawen Zhang, Shun Zheng, Wei Cao, Jiang Bian, Jia Li
Irregularly sampled multivariate time series are ubiquitous in various fields, particularly in healthcare, and exhibit two key characteristics: intra-series irregularity and inter-series discrepancy.
1 code implementation • 3 Jun 2023 • Hangting Ye, Zhining Liu, Xinyi Shen, Wei Cao, Shun Zheng, Xiaofan Gui, Huishuai Zhang, Yi Chang, Jiang Bian
This is a challenging task given the heterogeneous model structures and assumptions adopted by existing UAD methods.
no code implementations • 3 Sep 2022 • Yingtao Luo, Chang Xu, Yang Liu, Weiqing Liu, Shun Zheng, Jiang Bian
In this work, we propose an learning framework that can automatically obtain interpretable PDE models from sequential data.
no code implementations • 4 Jul 2022 • Tianping Zhang, Yizhuo Zhang, Wei Cao, Jiang Bian, Xiaohan Yi, Shun Zheng, Jian Li
It uses less than 5% FLOPS compared with previous SOTA methods on the largest benchmark dataset.
1 code implementation • ICLR 2022 • Wei Fan, Shun Zheng, Xiaohan Yi, Wei Cao, Yanjie Fu, Jiang Bian, Tie-Yan Liu
However, the complicated dependencies of the PTS signal on its inherent periodicity as well as the sophisticated composition of various periods hinder the performance of PTS forecasting.
no code implementations • 9 Apr 2021 • Disheng Tang, Wei Cao, Jiang Bian, Tie-Yan Liu, Zhifeng Gao, Shun Zheng, Jue Liu
We used a stochastic metapopulation model with a hierarchical structure and fitted the model to the positive cases in the US from the start of outbreak to the end of 2020.
no code implementations • 1 Jan 2021 • Yihan He, Wei Cao, Shun Zheng, Zhifeng Gao, Jiang Bian
In recent years, research communities have been developing stochastic sampling methods to handle large graphs when it is unreal to put the whole graph into a single batch.
1 code implementation • 1 Jan 2021 • Yihan He, Wei Cao, Shun Zheng, Zhifeng Gao, Jiang Bian
In this work, we present a new method named Fourier Temporal State Embedding (FTSE) to address the temporal information in dynamic graph representation learning.
1 code implementation • ACL 2020 • Wentao Xu, Shun Zheng, Liang He, Bin Shao, Jian Yin, Tie-Yan Liu
In recent years, knowledge graph embedding becomes a pretty hot research topic of artificial intelligence and plays increasingly vital roles in various downstream applications, such as recommendation and question answering.
Ranked #1 on Link Prediction on YAGO37
2 code implementations • IJCNLP 2019 • Shun Zheng, Wei Cao, Wei Xu, Jiang Bian
Most existing event extraction (EE) methods merely extract event arguments within the sentence scope.
Ranked #4 on Document-level Event Extraction on ChFinAnn
1 code implementation • ACL 2019 • Shun Zheng, Xu Han, Yankai Lin, Peilin Yu, Lu Chen, Ling Huang, Zhiyuan Liu, Wei Xu
To demonstrate the effectiveness of DIAG-NRE, we apply it to two real-world datasets and present both significant and interpretable improvements over state-of-the-art methods.
no code implementations • 13 Apr 2016 • Shun Zheng, Jialei Wang, Fen Xia, Wei Xu, Tong Zhang
In modern large-scale machine learning applications, the training data are often partitioned and stored on multiple machines.