6 citations · 8 across the 4 of their papers we have counts for
5 papers
Dynamic Programming Techniques for Enhancing Cognitive Representation in Knowledge Tracing
Lixiang Xu, Xianwei Ding, Xin Yuan +4
Knowledge Tracing (KT) involves monitoring the changes in a student's knowledge over time by analyzing their past responses, with the goal of predicting future performance. However…
Improving Question Embeddings with Cognitive Representation Optimization for Knowledge Tracing
Lixiang Xu, Xianwei Ding, Xin Yuan +5
Designed to track changes in students' knowledge status and predict their future answers based on students' historical answer records. Current research on KT modeling focuses on pr…
ENADPool: The Edge-Node Attention-based Differentiable Pooling for Graph Neural Networks
Zhehan Zhao, Lu Bai, Lixin Cui +4
Graph Neural Networks (GNNs) are powerful tools for graph classification. One important operation for GNNs is the downsampling or pooling that can learn effective embeddings from t…
AKBR: Learning Adaptive Kernel-based Representations for Graph Classification
Feifei Qian, Lixin Cui, Ming Li +6
In this paper, we propose a new model to learn Adaptive Kernel-based Representations (AKBR) for graph classification. Unlike state-of-the-art R-convolution graph kernels that are d…
Group Multi-View Transformer for 3D Shape Analysis with Spatial Encoding
Lixiang Xu, Qingzhe Cui, Richang Hong +5
In recent years, the results of view-based 3D shape recognition methods have saturated, and models with excellent performance cannot be deployed on memory-limited devices due to th…