309 citations · 1.6k across the 77 of their papers we have counts for
22 papers · 1 filter
Beyond Extrapolation: Knowledge Utilization Paradigm with Bidirectional Inspiration for Time Series Forecasting
Liu Chong, Yingjie Zhou, Hao Li +3
Time-series forecasting is critical in various scenarios, such as energy, transportation, and public health. However, most existing forecasters rely primarily on one-way inference,…
Learning Feature Encoder with Synthetic Anomalies for Weakly Supervised Graph Anomaly Detection
Yingjie Zhou, Yuqin Xie, Fanxing Liu +3
Weakly supervised graph anomaly detection aims to unveil unusual graph instances, e.g., nodes, whose behaviors significantly differ from normal ones, given only a limited number of…
Tensor Polynomial Additive Model
Yang Chen, Ce Zhu, Jiani Liu +1
Additive models can be used for interpretable machine learning for their clarity and simplicity. However, In the classical models for high-order data, the vectorization operation d…
Inverse-Free Fast Natural Gradient Descent Method for Deep Learning
Xinwei Ou, Ce Zhu, Xiaolin Huang +1
Second-order optimization techniques have the potential to achieve faster convergence rates compared to first-order methods through the incorporation of second-order derivatives or…
HOIN: High-Order Implicit Neural Representations
Yang Chen, Ruituo Wu, Yipeng Liu +1
Implicit neural representations (INR) suffer from worsening spectral bias, which results in overly smooth solutions to the inverse problem. To deal with this problem, we propose a…
S^2MVTC: a Simple yet Efficient Scalable Multi-View Tensor Clustering
Zhen Long, Qiyuan Wang, Yazhou Ren +2
Anchor-based large-scale multi-view clustering has attracted considerable attention for its effectiveness in handling massive datasets. However, current methods mainly seek the con…
TERM Model: Tensor Ring Mixture Model for Density Estimation
Ruituo Wu, Jiani Liu, Ce Zhu +3
Efficient probability density estimation is a core challenge in statistical machine learning. Tensor-based probabilistic graph methods address interpretability and stability concer…
Federated Deep Multi-View Clustering with Global Self-Supervision
Xinyue Chen, Jie Xu, Yazhou Ren +5
Federated multi-view clustering has the potential to learn a global clustering model from data distributed across multiple devices. In this setting, label information is unknown an…
Low-Rank Multitask Learning based on Tensorized SVMs and LSSVMs
Jiani Liu, Qinghua Tao, Ce Zhu +3
Multitask learning (MTL) leverages task-relatedness to enhance performance. With the emergence of multimodal data, tasks can now be referenced by multiple indices. In this paper, w…
Low Rank Optimization for Efficient Deep Learning: Making A Balance between Compact Architecture and Fast Training
Xinwei Ou, Zhangxin Chen, Ce Zhu +1
Deep neural networks have achieved great success in many data processing applications. However, the high computational complexity and storage cost makes deep learning hard to be us…
Tensorized LSSVMs for Multitask Regression
Jiani Liu, Qinghua Tao, Ce Zhu +2
Multitask learning (MTL) can utilize the relatedness between multiple tasks for performance improvement. The advent of multimodal data allows tasks to be referenced by multiple ind…
FedTADBench: Federated Time-Series Anomaly Detection Benchmark
Fanxing Liu, Cheng Zeng, Le Zhang +5
Time series anomaly detection strives to uncover potential abnormal behaviors and patterns from temporal data, and has fundamental significance in diverse application scenarios. Co…
Tucker-O-Minus Decomposition for Multi-view Tensor Subspace Clustering
Yingcong Lu, Yipeng Liu, Zhen Long +2
With powerful ability to exploit latent structure of self-representation information, different tensor decompositions have been employed into low rank multi-view clustering (LRMVC)…
Semi-tensor Product-based TensorDecomposition for Neural Network Compression
Hengling Zhao, Yipeng Liu, Xiaolin Huang +1
The existing tensor networks adopt conventional matrix product for connection. The classical matrix product requires strict dimensionality consistency between factors, which can re…
Feature Encoding with AutoEncoders for Weakly-supervised Anomaly Detection
Yingjie Zhou, Xucheng Song, Yanru Zhang +3
Weakly-supervised anomaly detection aims at learning an anomaly detector from a limited amount of labeled data and abundant unlabeled data. Recent works build deep neural networks…
Performance Evaluation of Adversarial Attacks: Discrepancies and Solutions
Jing Wu, Mingyi Zhou, Ce Zhu +3
Recently, adversarial attack methods have been developed to challenge the robustness of machine learning models. However, mainstream evaluation criteria experience limitations, eve…
Deep Embedded Multi-view Clustering with Collaborative Training
Jie Xu, Yazhou Ren, Guofeng Li +3
Multi-view clustering has attracted increasing attentions recently by utilizing information from multiple views. However, existing multi-view clustering methods are either with hig…
Unsupervised Feature Selection via Multi-step Markov Transition Probability
Yan Min, Mao Ye, Liang Tian +3
Feature selection is a widely used dimension reduction technique to select feature subsets because of its interpretability. Many methods have been proposed and achieved good result…
A Unified Framework for Coupled Tensor Completion
Huyan Huang, Yipeng Liu, Ce Zhu
Coupled tensor decomposition reveals the joint data structure by incorporating priori knowledge that come from the latent coupled factors. The tensor ring (TR) decomposition is inv…
Robust Low-Rank Tensor Ring Completion
Huyan Huang, Yipeng Liu, Ce Zhu
Low-rank tensor completion recovers missing entries based on different tensor decompositions. Due to its outstanding performance in exploiting some higher-order data structure, low…
Low-rank Tensor Grid for Image Completion
Huyan Huang, Yipeng Liu, Ce Zhu
Tensor completion estimates missing components by exploiting the low-rank structure of multi-way data. The recently proposed methods based on tensor train (TT) and tensor ring (TR)…
Provable Tensor Ring Completion
Huyan Huang, Yipeng Liu, Ce Zhu
Tensor completion recovers a multi-dimensional array from a limited number of measurements. Using the recently proposed tensor ring (TR) decomposition, in this paper we show that a…