98 citations · 264 across the 24 of their papers we have counts for
25 papers
TSAQA: Time Series Analysis Question And Answering Benchmark
Baoyu Jing, Sanhorn Chen, Lecheng Zheng +13
Time series data are integral to critical applications across domains such as finance, healthcare, transportation, and environmental science. While recent work has begun to explore…
From Images to Signals: Are Large Vision Models Useful for Time Series Analysis?
Ziming Zhao, ChengAo Shen, Hanghang Tong +4
Transformer-based models have gained increasing attention in time series research, driving interest in Large Language Models (LLMs) and foundation models for time series analysis.…
Multi-Modal View Enhanced Large Vision Models for Long-Term Time Series Forecasting
ChengAo Shen, Wenchao Yu, Ziming Zhao +4
Time series, typically represented as numerical sequences, can also be transformed into images and texts, offering multi-modal views (MMVs) of the same underlying signal. These MMV…
HERO: Preserving Structure and Semantics in Heterogeneous Continual Graph Learning
Guiquan Sun, Xikun Zhang, Jingchao Ni +1
Machine learning on heterogeneous graphs has advanced rapidly, driven by the diverse entities, relations, and semantics inherent in real-world data. However, most existing studies…
Multi-modal Time Series Analysis: A Tutorial and Survey
Yushan Jiang, Kanghui Ning, Zijie Pan +7
Multi-modal time series analysis has recently emerged as a prominent research area in data mining, driven by the increasing availability of diverse data modalities, such as text, i…
Harnessing Vision Models for Time Series Analysis: A Survey
Jingchao Ni, Ziming Zhao, ChengAo Shen +5
Time series analysis has witnessed the inspiring development from traditional autoregressive models, deep learning models, to recent Transformers and Large Language Models (LLMs).…
MELODY: Robust Semi-Supervised Hybrid Model for Entity-Level Online Anomaly Detection with Multivariate Time Series
Jingchao Ni, Gauthier Guinet, Peihong Jiang +2
In large IT systems, software deployment is a crucial process in online services as their code is regularly updated. However, a faulty code change may degrade the target service's…
Time Series Contrastive Learning with Information-Aware Augmentations
Dongsheng Luo, Wei Cheng, Yingheng Wang +8
Various contrastive learning approaches have been proposed in recent years and achieve significant empirical success. While effective and prevalent, contrastive learning has been l…
Hierarchical Graph Neural Networks for Causal Discovery and Root Cause Localization
Dongjie Wang, Zhengzhang Chen, Jingchao Ni +4
In this paper, we propose REASON, a novel framework that enables the automatic discovery of both intra-level (i.e., within-network) and inter-level (i.e., across-network) causal re…
Personalized Federated Learning via Heterogeneous Modular Networks
Tianchun Wang, Wei Cheng, Dongsheng Luo +5
Personalized Federated Learning (PFL) which collaboratively trains a federated model while considering local clients under privacy constraints has attracted much attention. Despite…
Do Multi-Lingual Pre-trained Language Models Reveal Consistent Token Attributions in Different Languages?
Junxiang Wang, Xuchao Zhang, Bo Zong +5
During the past several years, a surge of multi-lingual Pre-trained Language Models (PLMs) has been proposed to achieve state-of-the-art performance in many cross-lingual downstrea…
Zero-Shot Cross-Lingual Machine Reading Comprehension via Inter-sentence Dependency Graph
Liyan Xu, Xuchao Zhang, Bo Zong +6
We target the task of cross-lingual Machine Reading Comprehension (MRC) in the direct zero-shot setting, by incorporating syntactic features from Universal Dependencies (UD), and t…
Convolutional Transformer based Dual Discriminator Generative Adversarial Networks for Video Anomaly Detection
Xinyang Feng, Dongjin Song, Yuncong Chen +3
Detecting abnormal activities in real-world surveillance videos is an important yet challenging task as the prior knowledge about video anomalies is usually limited or unavailable.…
FACESEC: A Fine-grained Robustness Evaluation Framework for Face Recognition Systems
Liang Tong, Zhengzhang Chen, Jingchao Ni +4
We present FACESEC, a framework for fine-grained robustness evaluation of face recognition systems. FACESEC evaluation is performed along four dimensions of adversarial modeling: t…
Unsupervised Document Embedding via Contrastive Augmentation
Dongsheng Luo, Wei Cheng, Jingchao Ni +8
We present a contrasting learning approach with data augmentation techniques to learn document representations in an unsupervised manner. Inspired by recent contrastive self-superv…
Dynamic Gaussian Mixture based Deep Generative Model For Robust Forecasting on Sparse Multivariate Time Series
Yinjun Wu, Jingchao Ni, Wei Cheng +7
Forecasting on sparse multivariate time series (MTS) aims to model the predictors of future values of time series given their incomplete past, which is important for many emerging…
Learning to Drop: Robust Graph Neural Network via Topological Denoising
Dongsheng Luo, Wei Cheng, Wenchao Yu +4
Graph Neural Networks (GNNs) have shown to be powerful tools for graph analytics. The key idea is to recursively propagate and aggregate information along edges of the given graph.…
T-Net: A Semi-supervised Deep Model for Turbulence Forecasting
Denghui Zhang, Yanchi Liu, Wei Cheng +5
Accurate air turbulence forecasting can help airlines avoid hazardous turbulence, guide the routes that keep passengers safe, maximize efficiency, and reduce costs. Traditional tur…
Multi-Scale One-Class Recurrent Neural Networks for Discrete Event Sequence Anomaly Detection
Zhiwei Wang, Zhengzhang Chen, Jingchao Ni +3
Discrete event sequences are ubiquitous, such as an ordered event series of process interactions in Information and Communication Technology systems. Recent years have witnessed in…
Structural Temporal Graph Neural Networks for Anomaly Detection in Dynamic Graphs
Lei Cai, Zhengzhang Chen, Chen Luo +4
Detecting anomalies in dynamic graphs is a vital task, with numerous practical applications in areas such as security, finance, and social media. Previous network embedding based m…
Asymmetrical Hierarchical Networks with Attentive Interactions for Interpretable Review-Based Recommendation
Xin Dong, Jingchao Ni, Wei Cheng +6
Recently, recommender systems have been able to emit substantially improved recommendations by leveraging user-provided reviews. Existing methods typically merge all reviews of a g…
Heterogeneous Graph Matching Networks
Shen Wang, Zhengzhang Chen, Xiao Yu +7
Information systems have widely been the target of malware attacks. Traditional signature-based malicious program detection algorithms can only detect known malware and are prone t…
Adversarial Defense Framework for Graph Neural Network
Shen Wang, Zhengzhang Chen, Jingchao Ni +4
Graph neural network (GNN), as a powerful representation learning model on graph data, attracts much attention across various disciplines. However, recent studies show that GNN is…
Attentional Heterogeneous Graph Neural Network: Application to Program Reidentification
Shen Wang, Zhengzhang Chen, Ding Li +6
Program or process is an integral part of almost every IT/OT system. Can we trust the identity/ID (e.g., executable name) of the program? To avoid detection, malware may disguise i…