Search Results for author: Haifeng Chen

Found 43 papers, 18 papers with code

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

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.

Federated Learning Time Series +1

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.

Retrieval Time Series Classification

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.

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.

Association Entity Alignment

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

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 +2

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 +2

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.

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.

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

no code implementations 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 +3

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 +3

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.

Time Series

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 +3

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 +3

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 Anomaly Detection Unsupervised Anomaly Detection

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 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 Forecasting Time Series Prediction

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