OneShotSTL: One-Shot Seasonal-Trend Decomposition For Online Time Series Anomaly Detection And Forecasting

1 code implementation • 4 Apr 2023 • Xiao He, Ye Li, Jian Tan, Bin Wu, Feifei Li

Extensive experiments on real-world benchmark datasets for downstream time series anomaly detection and forecasting tasks demonstrate that OneShotSTL is from 10 to over 1, 000 times faster than the state-of-the-art methods, while still providing comparable or even better accuracy.

Anomaly Detection Time Series Anomaly Detection

A Unified and Efficient Coordinating Framework for Autonomous DBMS Tuning

no code implementations • 10 Mar 2023 • Xinyi Zhang, Zhuo Chang, Hong Wu, Yang Li, Jia Chen, Jian Tan, Feifei Li, Bin Cui

To tune different components for DBMS, a coordinating mechanism is needed to make the multiple agents cognizant of each other.

Thompson Sampling

Interval Type-2 Fuzzy Neural Networks for Multi-Label Classification

no code implementations • 21 Feb 2023 • Dayong Tian, Feifei Li, Yiwen Wei

We also propose a loss function to measure the similarities between binary labels in datasets and interval type-2 fuzzy memberships generated by our model.

Multi-Label Classification Vocal Bursts Type Prediction

TencentPretrain: A Scalable and Flexible Toolkit for Pre-training Models of Different Modalities

2 code implementations • 13 Dec 2022 • Zhe Zhao, Yudong Li, Cheng Hou, Jing Zhao, Rong Tian, Weijie Liu, Yiren Chen, Ningyuan Sun, Haoyan Liu, Weiquan Mao, Han Guo, Weigang Guo, Taiqiang Wu, Tao Zhu, Wenhang Shi, Chen Chen, Shan Huang, Sihong Chen, Liqun Liu, Feifei Li, Xiaoshuai Chen, Xingwu Sun, Zhanhui Kang, Xiaoyong Du, Linlin Shen, Kimmo Yan

The proposed pre-training models of different modalities are showing a rising trend of homogeneity in their model structures, which brings the opportunity to implement different pre-training models within a uniform framework.

LPC-AD: Fast and Accurate Multivariate Time Series Anomaly Detection via Latent Predictive Coding

no code implementations • 5 May 2022 • Zhi Qi, Hong Xie, Ye Li, Jian Tan, Feifei Li, John C. S. Lui

LPC-AD is motivated by the ever-increasing needs for fast and accurate MTS anomaly detection methods to support fast troubleshooting in cloud computing, micro-service systems, etc.

Anomaly Detection Time Series Anomaly Detection

Towards General Deep Leakage in Federated Learning

no code implementations • 18 Oct 2021 • Jiahui Geng, Yongli Mou, Feifei Li, Qing Li, Oya Beyan, Stefan Decker, Chunming Rong

We find that image restoration fails even if there is only one incorrectly inferred label in the batch; we also find that when batch images have the same label, the corresponding image is restored as a fusion of that class of images.

Federated Learning Image Restoration

Database Workload Characterization with Query Plan Encoders

3 code implementations • 26 May 2021 • Debjyoti Paul, Jie Cao, Feifei Li, Vivek Srikumar

To address this workload characterization problem, we propose our query plan encoders that learn essential features and their correlations from query plans.

Representation Learning Transfer Learning

Constrained Non-Affine Alignment of Embeddings

no code implementations • 13 Oct 2019 • Yuwei Wang, Yan Zheng, Yanqing Peng, Chin-Chia Michael Yeh, Zhongfang Zhuang, Das Mahashweta, Bendre Mangesh, Feifei Li, Wei zhang, Jeff M. Phillips

Embeddings are already essential tools for large language models and image analysis, and their use is being extended to many other research domains.