1 code implementation • 31 Mar 2024 • Qi Liu, Yan Zhuang, Haoyang Bi, Zhenya Huang, Weizhe Huang, Jiatong Li, Junhao Yu, Zirui Liu, Zirui Hu, Yuting Hong, Zachary A. Pardos, Haiping Ma, Mengxiao Zhu, Shijin Wang, Enhong Chen
Computerized Adaptive Testing (CAT) provides an efficient and tailored method for assessing the proficiency of examinees, by dynamically adjusting test questions based on their performance.
no code implementations • 13 Mar 2024 • Mingyue Cheng, Hao Zhang, Jiqian Yang, Qi Liu, Li Li, Xin Huang, Liwei Song, Zhi Li, Zhenya Huang, Enhong Chen
Through this gateway, users have the opportunity to submit their questions, testing the models on a personalized and potentially broader range of capabilities.
no code implementations • 12 Mar 2024 • Mingyue Cheng, Hao Zhang, Qi Liu, Fajie Yuan, Zhi Li, Zhenya Huang, Enhong Chen, Jun Zhou, Longfei Li
It is also significant to model the \textit{semantic relatedness} reflected in content features, e. g., images and text.
1 code implementation • 10 Mar 2024 • Fei Wang, Haoyu Liu, Haoyang Bi, Xiangzhuang Shen, Renyu Zhu, Runze Wu, Minmin Lin, Tangjie Lv, Changjie Fan, Qi Liu, Zhenya Huang, Enhong Chen
In this paper, we introduce a substantial crowdsourcing annotation dataset collected from a real-world crowdsourcing platform.
1 code implementation • 10 Mar 2024 • Liyang He, Zhenya Huang, Jiayu Liu, Enhong Chen, Fei Wang, Jing Sha, Shijin Wang
In this paper, we propose an innovative Bit-mask Robust Contrastive knowledge Distillation (BRCD) method, specifically devised for the distillation of semantic hashing models.
no code implementations • 9 Mar 2024 • Fei Wang, Qi Liu, Enhong Chen, Chuanren Liu, Zhenya Huang, Jinze Wu, Shijin Wang
Specifically, based on the idea of estimating the posterior distributions of cognitive diagnosis model parameters, we first provide a unified objective function for mini-batch based optimization that can be more efficiently applied to a wide range of models and large datasets.
1 code implementation • 21 Oct 2023 • Chuang Zhao, Hongke Zhao, HengShu Zhu, Zhenya Huang, Nan Feng, Enhong Chen, Hui Xiong
One prevalent solution is the bi-discriminator domain adversarial network, which strives to identify target domain samples outside the support of the source domain distribution and enforces their classification to be consistent on both discriminators.
no code implementations • 1 Sep 2023 • Jiatong Li, Qi Liu, Fei Wang, Jiayu Liu, Zhenya Huang, Fangzhou Yao, Linbo Zhu, Yu Su
However, we notice that this paradigm leads to the inevitable non-identifiability and explainability overfitting problem, which is harmful to the quantification of learners' cognitive states and the quality of web learning services.
1 code implementation • 14 Jul 2023 • Qi Liu, Zheng Gong, Zhenya Huang, Chuanren Liu, HengShu Zhu, Zhi Li, Enhong Chen, Hui Xiong
Machine learning algorithms have become ubiquitous in a number of applications (e. g. image classification).
1 code implementation • 18 Jun 2023 • Yan Zhuang, Qi Liu, Yuting Ning, Weizhe Huang, Rui Lv, Zhenya Huang, Guanhao Zhao, Zheng Zhang, Qingyang Mao, Shijin Wang, Enhong Chen
Different tests for different models using efficient adaptive testing -- we believe this has the potential to become a new norm in evaluating large language models.
no code implementations • 5 Apr 2023 • Shuanghong Shen, Enhong Chen, Bihan Xu, Qi Liu, Zhenya Huang, Linbo Zhu, Yu Su
In this paper, we present the Quiz-based Knowledge Tracing (QKT) model to monitor students' knowledge states according to their quiz-based learning interactions.
no code implementations • 11 Feb 2023 • Jiayu Liu, Zhenya Huang, ChengXiang Zhai, Qi Liu
In LeAp, we perform knowledge learning in a novel problem-knowledge-expression paradigm, with a Knowledge Encoder to acquire knowledge from problem data and a Knowledge Decoder to apply knowledge for expression reasoning.
no code implementations • 9 Feb 2023 • Yuting Ning, Jiayu Liu, Longhu Qin, Tong Xiao, Shangzi Xue, Zhenya Huang, Qi Liu, Enhong Chen, Jinze Wu
In the Natural Language for Optimization (NL4Opt) NeurIPS 2022 competition, competitors focus on improving the accessibility and usability of optimization solvers, with the aim of subtask 1: recognizing the semantic entities that correspond to the components of the optimization problem; subtask 2: generating formulations for the optimization problem.
1 code implementation • 18 Jan 2023 • Yuting Ning, Zhenya Huang, Xin Lin, Enhong Chen, Shiwei Tong, Zheng Gong, Shijin Wang
To this end, in this paper, we propose a novel contrastive pre-training approach for mathematical question representations, namely QuesCo, which attempts to bring questions with more similar purposes closer.
no code implementations • 16 Sep 2022 • Zaixi Zhang, Qi Liu, Zhenya Huang, Hao Wang, Chee-Kong Lee, Enhong Chen
One famous privacy attack against data analysis models is the model inversion attack, which aims to infer sensitive data in the training dataset and leads to great privacy concerns.
no code implementations • 18 May 2022 • Kai Zhang, Qi Liu, Zhenya Huang, Mingyue Cheng, Kun Zhang, Mengdi Zhang, Wei Wu, Enhong Chen
Existing studies in this task attach more attention to the sequence modeling of sentences while largely ignoring the rich domain-invariant semantics embedded in graph structures (i. e., the part-of-speech tags and dependency relations).
1 code implementation • 5 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.
1 code implementation • 6 May 2021 • Shuanghong Shen, Qi Liu, Zhenya Huang, Yonghe Zheng, Minghao Yin, Minjuan Wang, Enhong Chen
We hope that the current survey will assist both researchers and practitioners in fostering the development of KT, thereby benefiting a broader range of students.
no code implementations • 15 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.
1 code implementation • 7 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.
no code implementations • 11 Nov 2019 • Yunan Zhang, Xiang Cheng, Yufeng Zhang, Zihan Wang, Zhengqi Fang, Xiaoyan Wang, Zhenya Huang, ChengXiang Zhai
Answering complex questions involving multiple entities and relations is a challenging task.
1 code implementation • 23 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.
2 code implementations • 25 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.
Ranked #6 on Graph Regression on Lipophilicity
1 code implementation • 7 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.
no code implementations • 1 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.
no code implementations • 27 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.
no code implementations • 27 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.
no code implementations • 3 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.