Search Results for author: Bo Zong

Found 15 papers, 9 papers with code

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.

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 +1

Parameterized Explainer for Graph Neural Network

2 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.

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

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

Behavior Query Discovery in System-Generated Temporal Graphs

no code implementations18 Nov 2015 Bo Zong, Xusheng Xiao, Zhichun Li, Zhen-Yu Wu, Zhiyun Qian, Xifeng Yan, Ambuj K. Singh, Guofei Jiang

In this work, we investigate how to query temporal graphs and treat query formulation as a discriminative temporal graph pattern mining problem.

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