no code implementations • NeurIPS 2008 • Shuiwang Ji, Liang Sun, Rong Jin, Jieping Ye
We present a multi-label multiple kernel learning (MKL) formulation, in which the data are embedded into a low-dimensional space directed by the instance-label correlations encoded into a hypergraph.
no code implementations • NeurIPS 2009 • Shuai Huang, Jing Li, Liang Sun, Jun Liu, Teresa Wu, Kewei Chen, Adam Fleisher, Eric Reiman, Jieping Ye
Recent advances in neuroimaging techniques provide great potentials for effective diagnosis of Alzheimer’s disease (AD), the most common form of dementia.
no code implementations • NeurIPS 2009 • Liang Sun, Jun Liu, Jianhui Chen, Jieping Ye
MMV is an extension of the single measurement vector (SMV) model employed in standard compressive sensing (CS).
no code implementations • NeurIPS 2011 • Jun Liu, Liang Sun, Jieping Ye
In this paper, we show that such Euclidean projection problem admits an analytical solution and we develop a top-down algorithm where the key operation is to find the so-called \emph{maximal root-tree} of the subtree rooted at each node.
no code implementations • 31 May 2013 • James G. Scott, Liang Sun
We present a family of expectation-maximization (EM) algorithms for binary and negative-binomial logistic regression, drawing a sharp connection with the variational-Bayes algorithm of Jaakkola and Jordan (2000).
no code implementations • 26 Nov 2016 • Liang Sun, Jason Mielens, Jason Baldridge
Unsupervised models of dependency parsing typically require large amounts of clean, unlabeled data plus gold-standard part-of-speech tags.
no code implementations • 3 Mar 2018 • Hongwei Ge, Mingde Zhao, Liang Sun, Zhen Wang, Guozhen Tan, Qiang Zhang, C. L. Philip Chen
This paper proposes a many-objective optimization algorithm with two interacting processes: cascade clustering and reference point incremental learning (CLIA).
no code implementations • 27 Nov 2018 • Wei Wang, Shiyue He, Liang Sun, Tao Jiang, Qian Zhang
To this end, we propose DopplerFi, a communication framework that enables a two-way communication channel between BLE and Wi-Fi by injecting artificial Doppler shifts, which can be decoded by sensing the patterns in the Gaussian frequency shift keying (GFSK) demodulator and Channel State Information (CSI).
Networking and Internet Architecture
1 code implementation • 5 Dec 2018 • Qingsong Wen, Jingkun Gao, Xiaomin Song, Liang Sun, Huan Xu, Shenghuo Zhu
Based on the extracted trend, we apply the the non-local seasonal filtering to extract the seasonality component.
no code implementations • 30 Jan 2019 • Ming Lin, Shuang Qiu, Jieping Ye, Xiaomin Song, Qi Qian, Liang Sun, Shenghuo Zhu, Rong Jin
This bound is sub-optimal comparing to the information theoretical lower bound $\mathcal{O}(kd)$.
no code implementations • 8 May 2019 • Liang Sun, Bing Li, Chunfeng Yuan, Zheng-Jun Zha, Weiming Hu
Inspired by the fact that different modalities in videos carry complementary information, we propose a Multimodal Semantic Attention Network(MSAN), which is a new encoder-decoder framework incorporating multimodal semantic attributes for video captioning.
no code implementations • 3 Jun 2019 • Ming Lin, Xiaomin Song, Qi Qian, Hao Li, Liang Sun, Shenghuo Zhu, Rong Jin
We validate the superiority of the proposed method in our real-time high precision positioning system against several popular state-of-the-art robust regression methods.
1 code implementation • 10 Jun 2019 • Qingsong Wen, Jingkun Gao, Xiaomin Song, Liang Sun, Jian Tan
Extracting the underlying trend signal is a crucial step to facilitate time series analysis like forecasting and anomaly detection.
no code implementations • ICCV 2019 • Hongwei Ge, Zehang Yan, Kai Zhang, Mingde Zhao, Liang Sun
In the training process, the forward and backward LSTMs encode the succeeding and preceding words into their respective hidden states by simultaneously constructing the whole sentence in a complementary manner.
no code implementations • Remote Sensing (ISSN: 2072-4292), MDPI 2019 • Hongwei Zhao, Zhongxin Chen, Hao Jiang, Wenlong Jing, Liang Sun, Min Feng 6
First, we trained 1D CNNs, LSTM RNNs, and GRU RNNs based on the full images’ time series to attain three classifiers with optimal architectures and hyper-parameters.
no code implementations • 21 Feb 2020 • Jingkun Gao, Xiaomin Song, Qingsong Wen, Pichao Wang, Liang Sun, Huan Xu
It is deployed as a public online service and widely adopted in different business scenarios at Alibaba Group.
1 code implementation • 21 Feb 2020 • Qingsong Wen, Kai He, Liang Sun, Yingying Zhang, Min Ke, Huan Xu
Periodicity detection is a crucial step in time series tasks, including monitoring and forecasting of metrics in many areas, such as IoT applications and self-driving database management system.
no code implementations • 27 Feb 2020 • Qingsong Wen, Liang Sun, Fan Yang, Xiaomin Song, Jingkun Gao, Xue Wang, Huan Xu
In this paper, we systematically review different data augmentation methods for time series.
no code implementations • 7 May 2020 • Liang Sun, Zhanhao Mo, Fuhua Yan, Liming Xia, Fei Shan, Zhongxiang Ding, Wei Shao, Feng Shi, Huan Yuan, Huiting Jiang, Dijia Wu, Ying WEI, Yaozong Gao, Wanchun Gao, He Sui, Daoqiang Zhang, Dinggang Shen
We evaluated our proposed AFS-DF on COVID-19 dataset with 1495 patients of COVID-19 and 1027 patients of community acquired pneumonia (CAP).
no code implementations • 10 Aug 2020 • Xin Zhang, Liangxiu Han, Wenyong Zhu, Liang Sun, Daoqiang Zhang
Different from the existing approaches, the novelty of our approach is three-fold: 1) A Residual Self-Attention Deep Neural Network has been proposed to capture local, global and spatial information of MR images to improve diagnostic performance; 2) An explanation method using Gradient-based Localization Class Activation mapping (Grad-CAM) has been introduced to improve the explainable of the proposed method; 3) This work has provided a full end-to-end learning solution for automated disease diagnosis.
no code implementations • 12 Oct 2020 • Liang Sun, Xiang Guan, Yang Yang, Lei Zhang
Specially, we first conduct a text-embedded network to embed text feature into the discriminative image feature learning to get a embedded feature.
Fine-Grained Image Recognition Fine-Grained Visual Recognition +1
no code implementations • 3 Mar 2021 • Qingyang Xu, Qingsong Wen, Liang Sun
By incorporating the learned long-range structure, the second stage can enhance the prediction accuracy in the forecast horizon.
no code implementations • 21 Jul 2021 • Liang Sun, Leonardo Escamilla
This paper addresses task-allocation problems with uncertainty in situational awareness for distributed autonomous robots (DARs).
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.
no code implementations • 5 Nov 2021 • Yingying Zhang, Zhengxiong Guan, Huajie Qian, Leili Xu, Hengbo Liu, Qingsong Wen, Liang Sun, Junwei Jiang, Lunting Fan, Min Ke
As business of Alibaba expands across the world among various industries, higher standards are imposed on the service quality and reliability of big data cloud computing platforms which constitute the infrastructure of Alibaba Cloud.
3 code implementations • 30 Jan 2022 • Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, Rong Jin
Although Transformer-based methods have significantly improved state-of-the-art results for long-term series forecasting, they are not only computationally expensive but more importantly, are unable to capture the global view of time series (e. g. overall trend).
10 code implementations • 15 Feb 2022 • Qingsong Wen, Tian Zhou, Chaoli Zhang, Weiqi Chen, Ziqing Ma, Junchi Yan, Liang Sun
From the perspective of network structure, we summarize the adaptations and modifications that have been made to Transformers in order to accommodate the challenges in time series analysis.
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.
3 code implementations • 18 May 2022 • Tian Zhou, Ziqing Ma, Xue Wang, Qingsong Wen, Liang Sun, Tao Yao, Wotao Yin, Rong Jin
Recent studies have shown that deep learning models such as RNNs and Transformers have brought significant performance gains for long-term forecasting of time series because they effectively utilize historical information.
Ranked #3 on Time Series Forecasting on ETTh2 (96) Univariate
no code implementations • 7 Jun 2022 • Xiaomin Song, Qingsong Wen, Yan Li, Liang Sun
Dynamic time warping (DTW) is an effective dissimilarity measure in many time series applications.
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 • 24 Jun 2022 • Tian Zhou, Jianqing Zhu, Xue Wang, Ziqing Ma, Qingsong Wen, Liang Sun, Rong Jin
Various deep learning models, especially some latest Transformer-based approaches, have greatly improved the state-of-art performance for long-term time series forecasting. However, those transformer-based models suffer a severe deterioration performance with prolonged input length, which prohibits them from using extended historical info. Moreover, these methods tend to handle complex examples in long-term forecasting with increased model complexity, which often leads to a significant increase in computation and less robustness in performance(e. g., overfitting).
1 code implementation • 18 Oct 2022 • Chaoli Zhang, Tian Zhou, Qingsong Wen, Liang Sun
Time series anomaly detection is a challenging problem due to the complex temporal dependencies and the limited label data.
1 code implementation • 24 Oct 2022 • Chenxiao Yang, Qitian Wu, Qingsong Wen, Zhiqiang Zhou, Liang Sun, Junchi Yan
The goal of sequential event prediction is to estimate the next event based on a sequence of historical events, with applications to sequential recommendation, user behavior analysis and clinical treatment.
3 code implementations • 23 Feb 2023 • Tian Zhou, Peisong Niu, Xue Wang, Liang Sun, Rong Jin
The main challenge that blocks the development of pre-trained model for time series analysis is the lack of a large amount of data for training.
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 • 7 Mar 2023 • Zhiqiang Zhou, Chaoli Zhang, Lingna Ma, Jing Gu, Huajie Qian, Qingsong Wen, Liang Sun, Peng Li, Zhimin Tang
This paper discusses horizontal POD resources management in Alibaba Cloud Container Services with a newly deployed AI algorithm framework named AHPA -- the adaptive horizontal pod auto-scaling system.
no code implementations • 31 May 2023 • Zhixian Wang, Qingsong Wen, Chaoli Zhang, Liang Sun, Yi Wang
The uncertainties in load forecasting can be divided into two types: epistemic uncertainty and aleatoric uncertainty.
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.
1 code implementation • 14 Jun 2023 • Yanjun Zhao, Ziqing Ma, Tian Zhou, Liang Sun, Mengni Ye, Yi Qian
On the other hand, the long input sequence usually leads to large model size and high time complexity.
2 code implementations • 17 Jun 2023 • Yiyuan Yang, Chaoli Zhang, Tian Zhou, Qingsong Wen, Liang Sun
On the other hand, contrastive learning aims to find a representation that can clearly distinguish any instance from the others, which can bring a more natural and promising representation for time series anomaly detection.
1 code implementation • 14 Jul 2023 • Zhixian Wang, Qingsong Wen, Chaoli Zhang, Liang Sun, Leandro Von Krannichfeldt, Yi Wang
Based on this, we conducted extensive experiments on load data at different levels, providing a reference for researchers to compare different load forecasting models.
no code implementations • 28 Aug 2023 • Shikai Fang, Qingsong Wen, Yingtao Luo, Shandian Zhe, Liang Sun
More importantly, almost all methods assume the observations are sampled at regular time stamps, and fail to handle complex irregular sampled time series arising from different applications.
1 code implementation • NeurIPS 2023 • Yi-Fan Zhang, Qingsong Wen, Xue Wang, Weiqi Chen, Liang Sun, Zhang Zhang, Liang Wang, Rong Jin, Tieniu Tan
Online updating of time series forecasting models aims to address the concept drifting problem by efficiently updating forecasting models based on streaming data.
no code implementations • 9 Oct 2023 • Binqing Wu, Weiqi Chen, Wengwei Wang, Bingqing Peng, Liang Sun, Ling Chen
In addition, the interactions between weather factors are further complicated by the spatial dependencies between regions, which are influenced by varied terrain and atmospheric motions.
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.
1 code implementation • 24 Nov 2023 • Tian Zhou, Peisong Niu, Xue Wang, Liang Sun, Rong Jin
Despite the impressive achievements of pre-trained models in the fields of natural language processing (NLP) and computer vision (CV), progress in the domain of time series analysis has been limited.
1 code implementation • 6 Dec 2023 • Chao Chen, Tian Zhou, Yanjun Zhao, Hui Liu, Liang Sun, Rong Jin
Moreover, we approximate the sparse regression process using a blend of a two-layer MLP and an extensive codebook.
Ranked #5 on Traffic Prediction on BJTaxi
1 code implementation • 19 Dec 2023 • Pengwei Liu, Wenwei Wang, Bingqing Peng, Binqing Wu, Liang Sun
While widely recognized as one of the most substantial weather forecasting methodologies, Numerical Weather Prediction (NWP) usually suffers from relatively coarse resolution and inevitable bias due to tempo-spatial discretization, physical parametrization process, and computation limitation.
1 code implementation • 3 Feb 2024 • Hao Cheng, Qingsong Wen, Yang Liu, Liang Sun
Time series forecasting is an important and forefront task in many real-world applications.
no code implementations • 8 Feb 2024 • Yanjun Zhao, Tian Zhou, Chao Chen, Liang Sun, Yi Qian, Rong Jin
Time series analysis is vital for numerous applications, and transformers have become increasingly prominent in this domain.
Computational Efficiency Multivariate Time Series Forecasting +2
no code implementations • 8 Feb 2024 • Ziqing Ma, Wenwei Wang, Tian Zhou, Chao Chen, Bingqing Peng, Liang Sun, Rong Jin
Current research predominantly relies on historical solar power data or numerical weather prediction in a single-modality format, ignoring the complementary information provided in different modalities.
no code implementations • 8 Feb 2024 • Peisong Niu, Tian Zhou, Xue Wang, Liang Sun, Rong Jin
Time series forecasting is essential for many practical applications, with the adoption of transformer-based models on the rise due to their impressive performance in NLP and CV.
1 code implementation • 22 Mar 2024 • Yifan Zhang, Weiqi Chen, Zhaoyang Zhu, Dalin Qin, Liang Sun, Xue Wang, Qingsong Wen, Zhang Zhang, Liang Wang, Rong Jin
For the state-of-the-art (SOTA) model, the MSE is reduced by $33. 3\%$.