Search Results for author: Jingchao Ni

Found 18 papers, 6 papers with code

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

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

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

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.

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

no code implementations31 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

Structural Temporal Graph Neural Networks for Anomaly Detection in Dynamic Graphs

no code implementations15 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 +2

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

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 Unsupervised Anomaly Detection

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