1 code implementation • 9 Oct 2024 • Zhixian Wang, Linxiao Yang, Liang Sun, Qingsong Wen, Yi Wang
Time series analysis is widely used in many fields such as power energy, economics, and transportation, including different tasks such as forecasting, anomaly detection, classification, etc.
no code implementations • 1 Jul 2024 • Jiehui Zhou, Linxiao Yang, Xingyu Liu, Xinyue Gu, Liang Sun, Wei Chen
In this paper, we propose CURLS, a novel rule learning method leveraging HTE, which can effectively describe subgroups with significant treatment effects.
no code implementations • 31 May 2024 • Rui Ren, Jingbang Yang, Linxiao Yang, Xinyue Gu, Liang Sun
Compared with existing fault localization algorithms, our algorithm can adapt to the imbalanced fault scenario of change service, and provide interpretable fault causes which are easy to understand and verify.
no code implementations • 24 Oct 2023 • Linxiao Yang, Rui Ren, Xinyue Gu, Liang Sun
Electric load forecasting is an indispensable component of electric power system planning and management.
no code implementations • 14 Jun 2023 • Hengbo Liu, Ziqing Ma, Linxiao Yang, Tian Zhou, Rui Xia, Yi Wang, Qingsong Wen, Liang Sun
In this paper, we propose a novel forecasting framework, named Self-adaptive Decomposed Interpretable framework~(SaDI), which ensembles long-term trend, short-term trend, and period modelings to capture temporal characteristics in different components.
no code implementations • 6 Mar 2023 • Qingsong Wen, Linxiao Yang, Liang Sun
In this paper, we propose a robust and effective periodicity detection algorithm for time series with block missing data.
no code implementations • NeurIPS 2021 • Fan Yang, Kai He, Linxiao Yang, Hongxia Du, Jingbang Yang, Bo Yang, Liang Sun
The learning problem is framed as a subset selection task in which a subset of all possible rules needs to be selected to form an accurate and interpretable rule set.
no code implementations • 23 Feb 2022 • Chaoli Zhang, Zhiqiang Zhou, Yingying Zhang, Linxiao Yang, Kai He, Qingsong Wen, Liang Sun
Localizing the root cause of network faults is crucial to network operation and maintenance.
no code implementations • 18 Sep 2021 • Linxiao Yang, Qingsong Wen, Bo Yang, Liang Sun
Many real-world time series exhibit multiple seasonality with different lengths.
1 code implementation • NeurIPS 2019 • Ngoc-Trung Tran, Viet-Hung Tran, Ngoc-Bao Nguyen, Linxiao Yang, Ngai-Man Cheung
From the analysis, we identify issues of SS tasks which allow a severely mode-collapsed generator to excel the SS tasks.
Ranked #7 on
Image Generation
on ImageNet 32x32
1 code implementation • ICCV 2019 • Linxiao Yang, Ngai-Man Cheung, Jiaying Li, Jun Fang
Our idea is that graph information which captures local data structures is an excellent complement to deep GMM.
no code implementations • 5 Nov 2018 • Kaihui Liu, Jiayi Wang, Zhengli Xing, Linxiao Yang, Jun Fang
We propose a hierarchical prior model for low-rank phase retrieval, in which a Gaussian-Wishart hierarchical prior is placed on the underlying low-rank matrix to promote the low-rankness of the matrix.
no code implementations • 6 Nov 2017 • Hang Xiao, Zhengli Xing, Linxiao Yang, Jun Fang, Yanlun Wu
In this paper, we consider the block-sparse signals recovery problem in the context of multiple measurement vectors (MMV) with common row sparsity patterns.
no code implementations • 8 Aug 2017 • Linxiao Yang, Jun Fang, Huiping Duan, Hongbin Li, Bing Zeng
The problem of low rank matrix completion is considered in this paper.
1 code implementation • 12 Sep 2016 • Zhou Zhou, Jun Fang, Linxiao Yang, Hongbin Li, Zhi Chen, Rick S. Blum
Different from most existing studies that are concerned with narrowband channels, we consider estimation of wideband mmWave channels with frequency selectivity, which is more appropriate for mmWave MIMO-OFDM systems.
Information Theory Information Theory
no code implementations • 15 Nov 2015 • Linxiao Yang, Jun Fang, Hongbin Li, Bing Zeng
In this paper, we focus on Tucker decomposition which represents an Nth-order tensor in terms of N factor matrices and a core tensor via multilinear operations.
no code implementations • 7 Mar 2015 • Linxiao Yang, Jun Fang, Hong Cheng, Hongbin Li
In this paper, we propose a new hierarchical Bayesian model for dictionary learning, in which a Gaussian-inverse Gamma hierarchical prior is used to promote the sparsity of the representation.