169 citations · 369 across the 14 of their papers we have counts for
14 papers
Near-real-time monitoring of global ocean carbon sink
Piyu Ke, Xiaofan Gui, Wei Cao +11
Mitigation of climate change will highly rely on a carbon emission trajectory that achieves carbon neutrality by the 2050s. The ocean plays a critical role in modulating climate ch…
NuTime: Numerically Multi-Scaled Embedding for Large-Scale Time-Series Pretraining
Chenguo Lin, Xumeng Wen, Wei Cao +4
Recent research on time-series self-supervised models shows great promise in learning semantic representations. However, it has been limited to small-scale datasets, e.g., thousand…
Warpformer: A Multi-scale Modeling Approach for Irregular Clinical Time Series
Jiawen Zhang, Shun Zheng, Wei Cao +2
Irregularly sampled multivariate time series are ubiquitous in various fields, particularly in healthcare, and exhibit two key characteristics: intra-series irregularity and inter-…
UADB: Unsupervised Anomaly Detection Booster
Hangting Ye, Zhining Liu, Xinyi Shen +6
Unsupervised Anomaly Detection (UAD) is a key data mining problem owing to its wide real-world applications. Due to the complete absence of supervision signals, UAD methods rely on…
Less Is More: Fast Multivariate Time Series Forecasting with Light Sampling-oriented MLP Structures
Tianping Zhang, Yizhuo Zhang, Wei Cao +4
Multivariate time series forecasting has seen widely ranging applications in various domains, including finance, traffic, energy, and healthcare. To capture the sophisticated tempo…
DEPTS: Deep Expansion Learning for Periodic Time Series Forecasting
Wei Fan, Shun Zheng, Xiaohan Yi +4
Periodic time series (PTS) forecasting plays a crucial role in a variety of industries to foster critical tasks, such as early warning, pre-planning, resource scheduling, etc. Howe…
Handling Inter-class and Intra-class Imbalance in Class-imbalanced Learning
Zhining Liu, Pengfei Wei, Zhepei Wei +5
Class-imbalance is a common problem in machine learning practice. Typical Imbalanced Learning (IL) methods balance the data via intuitive class-wise resampling or reweighting. Howe…
IMBENS: Ensemble Class-imbalanced Learning in Python
Zhining Liu, Jian Kang, Hanghang Tong +1
imbalanced-ensemble, abbreviated as imbens, is an open-source Python toolbox for leveraging the power of ensemble learning to address the class imbalance problem. It provides stand…
Impact of pandemic fatigue on the spread of COVID-19: a mathematical modelling study
Disheng Tang, Wei Cao, Jiang Bian +4
In late-2020, many countries around the world faced another surge in number of confirmed cases of COVID-19, including United Kingdom, Canada, Brazil, United States, etc., which res…
Forecast with Forecasts: Diversity Matters
Yanfei Kang, Wei Cao, Fotios Petropoulos +1
Forecast combinations have been widely applied in the last few decades to improve forecasting. Estimating optimal weights that can outperform simple averages is not always an easy…
MESA: Boost Ensemble Imbalanced Learning with MEta-SAmpler
Zhining Liu, Pengfei Wei, Jing Jiang +3
Imbalanced learning (IL), i.e., learning unbiased models from class-imbalanced data, is a challenging problem. Typical IL methods including resampling and reweighting were designed…
Trimming the Sail: A Second-order Learning Paradigm for Stock Prediction
Chi Chen, Li Zhao, Wei Cao +2
Nowadays, machine learning methods have been widely used in stock prediction. Traditional approaches assume an identical data distribution, under which a learned model on the train…
Self-paced Ensemble for Highly Imbalanced Massive Data Classification
Zhining Liu, Wei Cao, Zhifeng Gao +4
Many real-world applications reveal difficulties in learning classifiers from imbalanced data. The rising big data era has been witnessing more classification tasks with large-scal…
Doc2EDAG: An End-to-End Document-level Framework for Chinese Financial Event Extraction
Shun Zheng, Wei Cao, Wei Xu +1
Most existing event extraction (EE) methods merely extract event arguments within the sentence scope. However, such sentence-level EE methods struggle to handle soaring amounts of…