Search Results for author: Haifeng Chen

Found 77 papers, 36 papers with code

Knockoff-Guided Feature Selection via A Single Pre-trained Reinforced Agent

1 code implementation6 Mar 2024 Xinyuan Wang, Dongjie Wang, Wangyang Ying, Rui Xie, Haifeng Chen, Yanjie Fu

A deep Q-network, pre-trained with the original features and their corresponding pseudo labels, is employed to improve the efficacy of the exploration process in feature selection.

feature selection Pseudo Label

Feature Selection as Deep Sequential Generative Learning

no code implementations6 Mar 2024 Wangyang Ying, Dongjie Wang, Haifeng Chen, Yanjie Fu

(2) We leverage the trained feature subset utility evaluator as a gradient provider to guide the identification of the optimal feature subset embedding;(3) We decode the optimal feature subset embedding to autoregressively generate the best feature selection decision sequence with autostop.

feature selection

InfuserKI: Enhancing Large Language Models with Knowledge Graphs via Infuser-Guided Knowledge Integration

no code implementations18 Feb 2024 Fali Wang, Runxue Bao, Suhang Wang, Wenchao Yu, Yanchi Liu, Wei Cheng, Haifeng Chen

Though Large Language Models (LLMs) have shown remarkable open-generation capabilities across diverse domains, they struggle with knowledge-intensive tasks.

Knowledge Graphs

Parametric Augmentation for Time Series Contrastive Learning

1 code implementation16 Feb 2024 Xu Zheng, Tianchun Wang, Wei Cheng, Aitian Ma, Haifeng Chen, Mo Sha, Dongsheng Luo

In this study, we address this gap by analyzing time series data augmentation using information theory and summarizing the most commonly adopted augmentations in a unified format.

Contrastive Learning Data Augmentation +2

Uncertainty Quantification for In-Context Learning of Large Language Models

1 code implementation15 Feb 2024 Chen Ling, Xujiang Zhao, Xuchao Zhang, Wei Cheng, Yanchi Liu, Yiyou Sun, Mika Oishi, Takao Osaki, Katsushi Matsuda, Jie Ji, Guangji Bai, Liang Zhao, Haifeng Chen

Existing works have been devoted to quantifying the uncertainty in LLM's response, but they often overlook the complex nature of LLMs and the uniqueness of in-context learning.

Hallucination In-Context Learning +1

Chatbot Meets Pipeline: Augment Large Language Model with Definite Finite Automaton

no code implementations6 Feb 2024 Yiyou Sun, Junjie Hu, Wei Cheng, Haifeng Chen

This paper introduces the Definite Finite Automaton augmented large language model (DFA-LLM), a novel framework designed to enhance the capabilities of conversational agents using large language models (LLMs).

Chatbot Language Modelling +2

Multi-modal Causal Structure Learning and Root Cause Analysis

no code implementations4 Feb 2024 Lecheng Zheng, Zhengzhang Chen, Jingrui He, Haifeng Chen

Effective root cause analysis (RCA) is vital for swiftly restoring services, minimizing losses, and ensuring the smooth operation and management of complex systems.

Causal Discovery Contrastive Learning +2

POND: Multi-Source Time Series Domain Adaptation with Information-Aware Prompt Tuning

no code implementations19 Dec 2023 Junxiang Wang, Guangji Bai, Wei Cheng, Zhengzhang Chen, Liang Zhao, Haifeng Chen

In order to tackle these challenges simultaneously, in this paper, we introduce PrOmpt-based domaiN Discrimination (POND), the first framework to utilize prompts for time series domain adaptation.

Domain Adaptation Human Activity Recognition +3

DyExplainer: Explainable Dynamic Graph Neural Networks

no code implementations25 Oct 2023 Tianchun Wang, Dongsheng Luo, Wei Cheng, Haifeng Chen, Xiang Zhang

Dynamic GNNs, with their ever-evolving graph structures, pose a unique challenge and require additional efforts to effectively capture temporal dependencies and structural relationships.

Contrastive Learning Link Prediction

A Survey on Detection of LLMs-Generated Content

1 code implementation24 Oct 2023 Xianjun Yang, Liangming Pan, Xuandong Zhao, Haifeng Chen, Linda Petzold, William Yang Wang, Wei Cheng

The burgeoning capabilities of advanced large language models (LLMs) such as ChatGPT have led to an increase in synthetic content generation with implications across a variety of sectors, including media, cybersecurity, public discourse, and education.

Open-ended Commonsense Reasoning with Unrestricted Answer Scope

no code implementations18 Oct 2023 Chen Ling, Xuchao Zhang, Xujiang Zhao, Yanchi Liu, Wei Cheng, Mika Oishi, Takao Osaki, Katsushi Matsuda, Haifeng Chen, Liang Zhao

In this work, we leverage pre-trained language models to iteratively retrieve reasoning paths on the external knowledge base, which does not require task-specific supervision.

Question Answering Retrieval

Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook

5 code implementations16 Oct 2023 Ming Jin, Qingsong Wen, Yuxuan Liang, Chaoli Zhang, Siqiao Xue, Xue Wang, James Zhang, Yi Wang, Haifeng Chen, XiaoLi Li, Shirui Pan, Vincent S. Tseng, Yu Zheng, Lei Chen, Hui Xiong

In this survey, we offer a comprehensive and up-to-date review of large models tailored (or adapted) for time series and spatio-temporal data, spanning four key facets: data types, model categories, model scopes, and application areas/tasks.

Time Series Time Series Analysis

A Survey of Heterogeneous Transfer Learning

1 code implementation12 Oct 2023 Runxue Bao, Yiming Sun, Yuhe Gao, Jindong Wang, Qiang Yang, Haifeng Chen, Zhi-Hong Mao, Ye Ye

These methods typically presuppose identical feature spaces and label spaces in both domains, known as homogeneous transfer learning, which, however, is not always a practical assumption.

Transfer Learning

Distantly-Supervised Joint Entity and Relation Extraction with Noise-Robust Learning

1 code implementation8 Oct 2023 Yufei Li, Xiao Yu, Yanghong Guo, Yanchi Liu, Haifeng Chen, Cong Liu

However, existing research primarily addresses only one type of noise, thereby limiting the effectiveness of noise reduction.

Joint Entity and Relation Extraction Relation

Zero-Shot Detection of Machine-Generated Codes

1 code implementation8 Oct 2023 Xianjun Yang, Kexun Zhang, Haifeng Chen, Linda Petzold, William Yang Wang, Wei Cheng

We then modify the previous zero-shot text detection method, DetectGPT (Mitchell et al., 2023) by utilizing a surrogate white-box model to estimate the probability of the rightmost tokens, allowing us to identify code snippets generated by language models.

Language Modelling Text Detection

Towards Robust Fidelity for Evaluating Explainability of Graph Neural Networks

1 code implementation3 Oct 2023 Xu Zheng, Farhad Shirani, Tianchun Wang, Wei Cheng, Zhuomin Chen, Haifeng Chen, Hua Wei, Dongsheng Luo

An explanation function for GNNs takes a pre-trained GNN along with a graph as input, to produce a `sufficient statistic' subgraph with respect to the graph label.

Decision Making

Large Language Models Can Be Good Privacy Protection Learners

no code implementations3 Oct 2023 Yijia Xiao, Yiqiao Jin, Yushi Bai, Yue Wu, Xianjun Yang, Xiao Luo, Wenchao Yu, Xujiang Zhao, Yanchi Liu, Haifeng Chen, Wei Wang, Wei Cheng

To address this challenge, we introduce Privacy Protection Language Models (PPLM), a novel paradigm for fine-tuning LLMs that effectively injects domain-specific knowledge while safeguarding data privacy.

Interpretable Imitation Learning with Dynamic Causal Relations

no code implementations30 Sep 2023 Tianxiang Zhao, Wenchao Yu, Suhang Wang, Lu Wang, Xiang Zhang, Yuncong Chen, Yanchi Liu, Wei Cheng, Haifeng Chen

After the model is learned, we can obtain causal relations among states and action variables behind its decisions, exposing policies learned by it.

Causal Discovery Imitation Learning

Pursuing Counterfactual Fairness via Sequential Autoencoder Across Domains

no code implementations22 Sep 2023 Yujie Lin, Chen Zhao, Minglai Shao, Baoluo Meng, Xujiang Zhao, Haifeng Chen

This approach effectively separates environmental information and sensitive attributes from the embedded representation of classification features.

Causal Inference counterfactual +2

GLAD: Content-aware Dynamic Graphs For Log Anomaly Detection

1 code implementation12 Sep 2023 Yufei Li, Yanchi Liu, Haoyu Wang, Zhengzhang Chen, Wei Cheng, Yuncong Chen, Wenchao Yu, Haifeng Chen, Cong Liu

Subsequently, GLAD utilizes a temporal-attentive graph edge anomaly detection model for identifying anomalous relations in these dynamic log graphs.

Anomaly Detection Few-Shot Learning

Adaptation Speed Analysis for Fairness-aware Causal Models

no code implementations31 Aug 2023 Yujie Lin, Chen Zhao, Minglai Shao, Xujiang Zhao, Haifeng Chen

In aligning p with p*, several factors can affect the adaptation rate, including the causal dependencies between variables in p. In real-life scenarios, however, we have to consider the fairness of the training process, and it is particularly crucial to involve a sensitive variable (bias) present between a cause and an effect variable.

Fairness Machine Translation +1

Skill Disentanglement for Imitation Learning from Suboptimal Demonstrations

1 code implementation13 Jun 2023 Tianxiang Zhao, Wenchao Yu, Suhang Wang, Lu Wang, Xiang Zhang, Yuncong Chen, Yanchi Liu, Wei Cheng, Haifeng Chen

Imitation learning has achieved great success in many sequential decision-making tasks, in which a neural agent is learned by imitating collected human demonstrations.

Disentanglement Imitation Learning

DNA-GPT: Divergent N-Gram Analysis for Training-Free Detection of GPT-Generated Text

1 code implementation27 May 2023 Xianjun Yang, Wei Cheng, Yue Wu, Linda Petzold, William Yang Wang, Haifeng Chen

However, this progress also presents a significant challenge in detecting the origin of a given text, and current research on detection methods lags behind the rapid evolution of LLMs.

Disentangled Causal Graph Learning for Online Unsupervised Root Cause Analysis

no code implementations18 May 2023 Dongjie Wang, Zhengzhang Chen, Yanjie Fu, Yanchi Liu, Haifeng Chen

In this paper, we propose CORAL, a novel online RCA framework that can automatically trigger the RCA process and incrementally update the RCA model.

Graph Learning

Uncertainty-Aware Bootstrap Learning for Joint Extraction on Distantly-Supervised Data

1 code implementation5 May 2023 Yufei Li, Xiao Yu, Yanchi Liu, Haifeng Chen, Cong Liu

To mitigate such impact, we propose uncertainty-aware bootstrap learning, which is motivated by the intuition that the higher uncertainty of an instance, the more likely the model confidence is inconsistent with the ground truths.

Relation Extraction

Personalized Federated Learning under Mixture of Distributions

1 code implementation1 May 2023 Yue Wu, Shuaicheng Zhang, Wenchao Yu, Yanchi Liu, Quanquan Gu, Dawei Zhou, Haifeng Chen, Wei Cheng

The recent trend towards Personalized Federated Learning (PFL) has garnered significant attention as it allows for the training of models that are tailored to each client while maintaining data privacy.

Personalized Federated Learning Uncertainty Quantification

Time Series Contrastive Learning with Information-Aware Augmentations

1 code implementation21 Mar 2023 Dongsheng Luo, Wei Cheng, Yingheng Wang, Dongkuan Xu, Jingchao Ni, Wenchao Yu, Xuchao Zhang, Yanchi Liu, Yuncong Chen, Haifeng Chen, Xiang Zhang

A key component of contrastive learning is to select appropriate augmentations imposing some priors to construct feasible positive samples, such that an encoder can be trained to learn robust and discriminative representations.

Contrastive Learning Open-Ended Question Answering +2

Dynamic Prompting: A Unified Framework for Prompt Tuning

1 code implementation6 Mar 2023 Xianjun Yang, Wei Cheng, Xujiang Zhao, Wenchao Yu, Linda Petzold, Haifeng Chen

Experimental results underscore the significant performance improvement achieved by dynamic prompt tuning across a wide range of tasks, including NLP tasks, vision recognition tasks, and vision-language tasks.


Exploring the Limits of ChatGPT for Query or Aspect-based Text Summarization

no code implementations16 Feb 2023 Xianjun Yang, Yan Li, Xinlu Zhang, Haifeng Chen, Wei Cheng

Text summarization has been a crucial problem in natural language processing (NLP) for several decades.

Abstractive Text Summarization

Knowledge-enhanced Neural Machine Reasoning: A Review

no code implementations4 Feb 2023 Tanmoy Chowdhury, Chen Ling, Xuchao Zhang, Xujiang Zhao, Guangji Bai, Jian Pei, Haifeng Chen, Liang Zhao

Knowledge-enhanced neural machine reasoning has garnered significant attention as a cutting-edge yet challenging research area with numerous practical applications.

DeepGAR: Deep Graph Learning for Analogical Reasoning

1 code implementation19 Nov 2022 Chen Ling, Tanmoy Chowdhury, Junji Jiang, Junxiang Wang, Xuchao Zhang, Haifeng Chen, Liang Zhao

As the most well-known computational method of analogical reasoning, Structure-Mapping Theory (SMT) abstracts both target and base subjects into relational graphs and forms the cognitive process of analogical reasoning by finding a corresponding subgraph (i. e., correspondence) in the target graph that is aligned with the base graph.

Graph Learning

Personalized Federated Learning via Heterogeneous Modular Networks

1 code implementation26 Oct 2022 Tianchun Wang, Wei Cheng, Dongsheng Luo, Wenchao Yu, Jingchao Ni, Liang Tong, Haifeng Chen, Xiang Zhang

Personalized Federated Learning (PFL) which collaboratively trains a federated model while considering local clients under privacy constraints has attracted much attention.

Personalized Federated Learning

CAT: Beyond Efficient Transformer for Content-Aware Anomaly Detection in Event Sequences

1 code implementation ACM SIGKDD Conference on Knowledge Discovery and Data Mining 2022 Shengming Zhang, Yanchi Liu, Xuchao Zhang, Wei Cheng, Haifeng Chen, Hui Xiong

It is critical and important to detect anomalies in event sequences, which becomes widely available in many application domains. In-deed, various efforts have been made to capture abnormal patterns from event sequences through sequential pattern analysis or event representation learning. However, existing approaches usually ignore the semantic information of event content. To this end, in this paper, we propose a self-attentive encoder-decoder transformer framework, Content-Aware Transformer(CAT), for anomaly detection in event sequences. In CAT, the encoder learns preamble event sequence representations with content awareness, and the decoder embeds sequences under detection into a latent space, where anomalies are distinguishable. Specifically, the event content is first fed to a content-awareness layer, generating representations of each event. The encoder accepts preamble event representation sequence, generating feature maps. In the decoder, an additional token is added at the beginning of the sequence under detection, denoting the sequence status. A one-class objective together with sequence reconstruction loss is collectively applied to train our framework under the label efficiency scheme. Furthermore, CAT is optimized under a scalable and efficient setting. Finally, extensive experiments on three real-world datasets demonstrate the superiority of CAT.

Anomaly Detection

Deep Federated Anomaly Detection for Multivariate Time Series Data

no code implementations9 May 2022 Wei Zhu, Dongjin Song, Yuncong Chen, Wei Cheng, Bo Zong, Takehiko Mizoguchi, Cristian Lumezanu, Haifeng Chen, Jiebo Luo

Specifically, we first design an Exemplar-based Deep Neural network (ExDNN) to learn local time series representations based on their compatibility with an exemplar module which consists of hidden parameters learned to capture varieties of normal patterns on each edge device.

Constrained Clustering Federated Learning +3

SEED: Sound Event Early Detection via Evidential Uncertainty

no code implementations5 Feb 2022 Xujiang Zhao, Xuchao Zhang, Wei Cheng, Wenchao Yu, Yuncong Chen, Haifeng Chen, Feng Chen

Sound Event Early Detection (SEED) is an essential task in recognizing the acoustic environments and soundscapes.

Event Detection Sound Event Detection

Ordinal-Quadruplet: Retrieval of Missing Classes in Ordinal Time Series

no code implementations24 Jan 2022 Jurijs Nazarovs, Cristian Lumezanu, Qianying Ren, Yuncong Chen, Takehiko Mizoguchi, Dongjin Song, Haifeng Chen

In this paper, we propose an ordered time series classification framework that is robust against missing classes in the training data, i. e., during testing we can prescribe classes that are missing during training.

Missing Labels Retrieval +3

Do Multi-Lingual Pre-trained Language Models Reveal Consistent Token Attributions in Different Languages?

no code implementations23 Dec 2021 Junxiang Wang, Xuchao Zhang, Bo Zong, Yanchi Liu, Wei Cheng, Jingchao Ni, Haifeng Chen, Liang Zhao

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 downstream tasks.

Zero-Shot Cross-Lingual Machine Reading Comprehension via Inter-sentence Dependency Graph

1 code implementation1 Dec 2021 Liyan Xu, Xuchao Zhang, Bo Zong, Yanchi Liu, Wei Cheng, Jingchao Ni, Haifeng Chen, Liang Zhao, Jinho D. Choi

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 the key features we use are the syntactic relations within each sentence.

Machine Reading Comprehension Sentence

Domain-oriented Language Pre-training with Adaptive Hybrid Masking and Optimal Transport Alignment

no code implementations1 Dec 2021 Denghui Zhang, Zixuan Yuan, Yanchi Liu, Hao liu, Fuzhen Zhuang, Hui Xiong, Haifeng Chen

Also, the word co-occurrences guided semantic learning of pre-training models can be largely augmented by entity-level association knowledge.

Entity Alignment

InfoGCL: Information-Aware Graph Contrastive Learning

no code implementations NeurIPS 2021 Dongkuan Xu, Wei Cheng, Dongsheng Luo, Haifeng Chen, Xiang Zhang

The key point of this framework is to follow the Information Bottleneck principle to reduce the mutual information between contrastive parts while keeping task-relevant information intact at both the levels of the individual module and the entire framework so that the information loss during graph representation learning can be minimized.

Contrastive Learning Graph Classification +3

Code Editing from Few Exemplars by Adaptive Multi-Extent Composition

no code implementations29 Sep 2021 Peizhao Li, Xuchao Zhang, Ziyu Yao, Wei Cheng, Haifeng Chen, Hongfu Liu

To achieve this, we propose a machine learning approach to adapt the editorial style derived from few exemplars to a query code snippet.

Superclass-Conditional Gaussian Mixture Model For Learning Fine-Grained Embeddings

1 code implementation ICLR 2022 Jingchao Ni, Wei Cheng, Zhengzhang Chen, Takayoshi Asakura, Tomoya Soma, Sho Kato, Haifeng Chen

The dilemma necessitates the adaptation of a "coarsely" pretrained model to new tasks with a few unseen "finer-grained" training labels.

Information-Aware Time Series Meta-Contrastive Learning

no code implementations29 Sep 2021 Dongsheng Luo, Wei Cheng, Yingheng Wang, Dongkuan Xu, Jingchao Ni, Wenchao Yu, Xuchao Zhang, Yanchi Liu, Haifeng Chen, Xiang Zhang

How to find the desired augmentations of time series data that are meaningful for given contrastive learning tasks and datasets remains an open question.

Contrastive Learning Meta-Learning +4

Recommend for a Reason: Unlocking the Power of Unsupervised Aspect-Sentiment Co-Extraction

1 code implementation Findings (EMNLP) 2021 Zeyu Li, Wei Cheng, Reema Kshetramade, John Houser, Haifeng Chen, Wei Wang

Compliments and concerns in reviews are valuable for understanding users' shopping interests and their opinions with respect to specific aspects of certain items.

Boosting Cross-Lingual Transfer via Self-Learning with Uncertainty Estimation

1 code implementation EMNLP 2021 Liyan Xu, Xuchao Zhang, Xujiang Zhao, Haifeng Chen, Feng Chen, Jinho D. Choi

Recent multilingual pre-trained language models have achieved remarkable zero-shot performance, where the model is only finetuned on one source language and directly evaluated on target languages.

Cross-Lingual Transfer named-entity-recognition +4

Convolutional Transformer based Dual Discriminator Generative Adversarial Networks for Video Anomaly Detection

no code implementations29 Jul 2021 Xinyang Feng, Dongjin Song, Yuncong Chen, Zhengzhang Chen, Jingchao Ni, Haifeng Chen

Next, a dual discriminator based adversarial training procedure, which jointly considers an image discriminator that can maintain the local consistency at frame-level and a video discriminator that can enforce the global coherence of temporal dynamics, is employed to enhance the future frame prediction.

Anomaly Detection Video Anomaly Detection

Learning Disentangled Representations for Time Series

no code implementations17 May 2021 Yuening Li, Zhengzhang Chen, Daochen Zha, Mengnan Du, Denghui Zhang, Haifeng Chen, Xia Hu

Motivated by the success of disentangled representation learning in computer vision, we study the possibility of learning semantic-rich time-series representations, which remains unexplored due to three main challenges: 1) sequential data structure introduces complex temporal correlations and makes the latent representations hard to interpret, 2) sequential models suffer from KL vanishing problem, and 3) interpretable semantic concepts for time-series often rely on multiple factors instead of individuals.

Disentanglement Time Series +1

FACESEC: A Fine-grained Robustness Evaluation Framework for Face Recognition Systems

1 code implementation CVPR 2021 Liang Tong, Zhengzhang Chen, Jingchao Ni, Wei Cheng, Dongjin Song, Haifeng Chen, Yevgeniy Vorobeychik

Moreover, we observe that open-set face recognition systems are more vulnerable than closed-set systems under different types of attacks.

Face Recognition

Unsupervised Document Embedding via Contrastive Augmentation

1 code implementation26 Mar 2021 Dongsheng Luo, Wei Cheng, Jingchao Ni, Wenchao Yu, Xuchao Zhang, Bo Zong, Yanchi Liu, Zhengzhang Chen, Dongjin Song, Haifeng Chen, Xiang Zhang

We present a contrasting learning approach with data augmentation techniques to learn document representations in an unsupervised manner.

Contrastive Learning Data Augmentation +4

Dynamic Gaussian Mixture based Deep Generative Model For Robust Forecasting on Sparse Multivariate Time Series

1 code implementation3 Mar 2021 Yinjun Wu, Jingchao Ni, Wei Cheng, Bo Zong, Dongjin Song, Zhengzhang Chen, Yanchi Liu, Xuchao Zhang, Haifeng Chen, Susan Davidson

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 applications.

Clustering Time Series +1

Aspect-based Sentiment Classification via Reinforcement Learning

no code implementations1 Jan 2021 Lichen Wang, Bo Zong, Yunyu Liu, Can Qin, Wei Cheng, Wenchao Yu, Xuchao Zhang, Haifeng Chen, Yun Fu

As texts always contain a large proportion of task-irrelevant words, accurate alignment between aspects and their sentimental descriptions is the most crucial and challenging step.

Classification General Classification +4

Parameterized Explainer for Graph Neural Network

3 code implementations NeurIPS 2020 Dongsheng Luo, Wei Cheng, Dongkuan Xu, Wenchao Yu, Bo Zong, Haifeng Chen, Xiang Zhang

The unique explanation interpreting each instance independently is not sufficient to provide a global understanding of the learned GNN model, leading to a lack of generalizability and hindering it from being used in the inductive setting.

Graph Classification

T$^2$-Net: A Semi-supervised Deep Model for Turbulence Forecasting

no code implementations26 Oct 2020 Denghui Zhang, Yanchi Liu, Wei Cheng, Bo Zong, Jingchao Ni, Zhengzhang Chen, Haifeng Chen, Hui Xiong

Accurate air turbulence forecasting can help airlines avoid hazardous turbulence, guide the routes that keep passengers safe, maximize efficiency, and reduce costs.

E-BERT: A Phrase and Product Knowledge Enhanced Language Model for E-commerce

no code implementations7 Sep 2020 Denghui Zhang, Zixuan Yuan, Yanchi Liu, Fuzhen Zhuang, Haifeng Chen, Hui Xiong

Pre-trained language models such as BERT have achieved great success in a broad range of natural language processing tasks.

Aspect Extraction Denoising +4

Multi-Scale One-Class Recurrent Neural Networks for Discrete Event Sequence Anomaly Detection

1 code implementation31 Aug 2020 Zhiwei Wang, Zhengzhang Chen, Jingchao Ni, Hui Liu, Haifeng Chen, Jiliang Tang

To address these challenges, in this paper, we propose OC4Seq, a multi-scale one-class recurrent neural network for detecting anomalies in discrete event sequences.

Anomaly Detection

AutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learning

no code implementations19 Jun 2020 Yuening Li, Zhengzhang Chen, Daochen Zha, Kaixiong Zhou, Haifeng Jin, Haifeng Chen, Xia Hu

Outlier detection is an important data mining task with numerous practical applications such as intrusion detection, credit card fraud detection, and video surveillance.

Fraud Detection Image Classification +7

Structural Temporal Graph Neural Networks for Anomaly Detection in Dynamic Graphs

1 code implementation15 May 2020 Lei Cai, Zhengzhang Chen, Chen Luo, Jiaping Gui, Jingchao Ni, Ding Li, Haifeng Chen

Detecting anomalies in dynamic graphs is a vital task, with numerous practical applications in areas such as security, finance, and social media.

Anomaly Detection Network Embedding

Inductive and Unsupervised Representation Learning on Graph Structured Objects

no code implementations ICLR 2020 Lichen Wang, Bo Zong, Qianqian Ma, Wei Cheng, Jingchao Ni, Wenchao Yu, Yanchi Liu, Dongjin Song, Haifeng Chen, Yun Fu

Inductive and unsupervised graph learning is a critical technique for predictive or information retrieval tasks where label information is difficult to obtain.

Graph Learning Graph Similarity +3

Asymmetrical Hierarchical Networks with Attentive Interactions for Interpretable Review-Based Recommendation

no code implementations18 Dec 2019 Xin Dong, Jingchao Ni, Wei Cheng, Zhengzhang Chen, Bo Zong, Dongjin Song, Yanchi Liu, Haifeng Chen, Gerard de Melo

In practice, however, these two sets of reviews are notably different: users' reviews reflect a variety of items that they have bought and are hence very heterogeneous in their topics, while an item's reviews pertain only to that single item and are thus topically homogeneous.

Recommendation Systems

Learning Robust Representations with Graph Denoising Policy Network

no code implementations4 Oct 2019 Lu Wang, Wenchao Yu, Wei Wang, Wei Cheng, Wei zhang, Hongyuan Zha, Xiaofeng He, Haifeng Chen

Graph representation learning, aiming to learn low-dimensional representations which capture the geometric dependencies between nodes in the original graph, has gained increasing popularity in a variety of graph analysis tasks, including node classification and link prediction.

Denoising Graph Representation Learning +2

A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data

5 code implementations20 Nov 2018 Chuxu Zhang, Dongjin Song, Yuncong Chen, Xinyang Feng, Cristian Lumezanu, Wei Cheng, Jingchao Ni, Bo Zong, Haifeng Chen, Nitesh V. Chawla

Subsequently, given the signature matrices, a convolutional encoder is employed to encode the inter-sensor (time series) correlations and an attention based Convolutional Long-Short Term Memory (ConvLSTM) network is developed to capture the temporal patterns.

Time Series Time Series Anomaly Detection +1

Learning Deep Network Representations with Adversarially Regularized Autoencoders

1 code implementation ACM SIGKDD International Conference on Knowledge Discovery & Data Mining 2018 Wenchao Yu, Cheng Zheng, Wei Cheng, Charu C. Aggarwal, Dongjin Song, Bo Zong, Haifeng Chen, Wei Wang

The problem of network representation learning, also known as network embedding, arises in many machine learning tasks assuming that there exist a small number of variabilities in the vertex representations which can capture the "semantics" of the original network structure.

Link Prediction Multi-Label Classification +1

A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction

14 code implementations7 Apr 2017 Yao Qin, Dongjin Song, Haifeng Chen, Wei Cheng, Guofei Jiang, Garrison Cottrell

The Nonlinear autoregressive exogenous (NARX) model, which predicts the current value of a time series based upon its previous values as well as the current and past values of multiple driving (exogenous) series, has been studied for decades.

Time Series Time Series Prediction

Robust Online Time Series Prediction with Recurrent Neural Networks

no code implementations26 Dec 2016 Tian Guo, Zhao Xu, Xin Yao, Haifeng Chen, Karl Aberer, Koichi Funaya

Time series forecasting for streaming data plays an important role in many real applications, ranging from IoT systems, cyber-networks, to industrial systems and healthcare.

Time Series Time Series Forecasting +1

Cannot find the paper you are looking for? You can Submit a new open access paper.