Search Results for author: Zhenya Huang

Found 12 papers, 6 papers with code

GraphMI: Extracting Private Graph Data from Graph Neural Networks

1 code implementation5 Jun 2021 Zaixi Zhang, Qi Liu, Zhenya Huang, Hao Wang, Chengqiang Lu, Chuanren Liu, Enhong Chen

Then we design a graph auto-encoder module to efficiently exploit graph topology, node attributes, and target model parameters for edge inference.

A Survey of Knowledge Tracing

1 code implementation6 May 2021 Qi Liu, Shuanghong Shen, Zhenya Huang, Enhong Chen, Yonghe Zheng

The recent COVID-19 epidemic has triggered the outbreak of online education, which has enabled both students and teachers to learn and teach at home.

Knowledge Tracing online learning

Quality meets Diversity: A Model-Agnostic Framework for Computerized Adaptive Testing

no code implementations15 Jan 2021 Haoyang Bi, Haiping Ma, Zhenya Huang, Yu Yin, Qi Liu, Enhong Chen, Yu Su, Shijin Wang

In this paper, we study a novel model-agnostic CAT problem, where we aim to propose a flexible framework that can adapt to different cognitive models.

Active Learning

ASGN: An Active Semi-supervised Graph Neural Network for Molecular Property Prediction

1 code implementation7 Jul 2020 Zhongkai Hao, Chengqiang Lu, Zheyuan Hu, Hao Wang, Zhenya Huang, Qi Liu, Enhong Chen, Cheekong Lee

Here we propose a novel framework called Active Semi-supervised Graph Neural Network (ASGN) by incorporating both labeled and unlabeled molecules.

Active Learning Molecular Property Prediction

Neural Cognitive Diagnosis for Intelligent Education Systems

1 code implementation23 Aug 2019 Fei Wang, Qi Liu, Enhong Chen, Zhenya Huang, Yuying Chen, Yu Yin, Zai Huang, Shijin Wang

Cognitive diagnosis is a fundamental issue in intelligent education, which aims to discover the proficiency level of students on specific knowledge concepts.

Molecular Property Prediction: A Multilevel Quantum Interactions Modeling Perspective

2 code implementations25 Jun 2019 Chengqiang Lu, Qi Liu, Chao Wang, Zhenya Huang, Peize Lin, Lixin He

In this paper, we propose a generalizable and transferable Multilevel Graph Convolutional neural Network (MGCN) for molecular property prediction.

Graph Regression Molecular Property Prediction

EKT: Exercise-aware Knowledge Tracing for Student Performance Prediction

1 code implementation7 Jun 2019 Qi Liu, Zhenya Huang, Yu Yin, Enhong Chen, Hui Xiong, Yu Su, Guoping Hu

In EERNN, we simply summarize each student's state into an integrated vector and trace it with a recurrent neural network, where we design a bidirectional LSTM to learn the encoding of each exercise's content.

Knowledge Tracing

Promotion of Answer Value Measurement with Domain Effects in Community Question Answering Systems

no code implementations1 Jun 2019 Binbin Jin, Enhong Chen, Hongke Zhao, Zhenya Huang, Qi Liu, HengShu Zhu, Shui Yu

Existing solutions mainly exploit the syntactic or semantic correlation between a question and its related answers (Q&A), where the multi-facet domain effects in CQA are still underexplored.

Answer Selection Community Question Answering

QuesNet: A Unified Representation for Heterogeneous Test Questions

no code implementations27 May 2019 Yu Yin, Qi Liu, Zhenya Huang, Enhong Chen, Wei Tong, Shijin Wang, Yu Su

Then we propose a two-level hierarchical pre-training algorithm to learn better understanding of test questions in an unsupervised way.

Language Modelling online learning

Transcribing Content from Structural Images with Spotlight Mechanism

no code implementations27 May 2019 Yu Yin, Zhenya Huang, Enhong Chen, Qi Liu, Fuzheng Zhang, Xing Xie, Guoping Hu

Then, we decide "what-to-write" by developing a GRU based network with the spotlight areas for transcribing the content accordingly.

Learning from History and Present: Next-item Recommendation via Discriminatively Exploiting User Behaviors

no code implementations3 Aug 2018 Zhi Li, Hongke Zhao, Qi Liu, Zhenya Huang, Tao Mei, Enhong Chen

In this paper, we propose a novel Behavior-Intensive Neural Network (BINN) for next-item recommendation by incorporating both users' historical stable preferences and present consumption motivations.

Session-Based Recommendations

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