111 citations · 380 across the 48 of their papers we have counts for
13 papers · 1 filter
Improving Interpretability of Deep Sequential Knowledge Tracing Models with Question-centric Cognitive Representations
Jiahao Chen, Zitao Liu, Shuyan Huang +2
Knowledge tracing (KT) is a crucial technique to predict students' future performance by observing their historical learning processes. Due to the powerful representation ability o…
simpleKT: A Simple But Tough-to-Beat Baseline for Knowledge Tracing
Zitao Liu, Qiongqiong Liu, Jiahao Chen +2
Knowledge tracing (KT) is the problem of predicting students' future performance based on their historical interactions with intelligent tutoring systems. Recently, many works pres…
Probabilistic Categorical Adversarial Attack & Adversarial Training
Han Xu, Pengfei He, Jie Ren +4
The existence of adversarial examples brings huge concern for people to apply Deep Neural Networks (DNNs) in safety-critical tasks. However, how to generate adversarial examples wi…
pyKT: A Python Library to Benchmark Deep Learning based Knowledge Tracing Models
Zitao Liu, Qiongqiong Liu, Jiahao Chen +3
Knowledge tracing (KT) is the task of using students' historical learning interaction data to model their knowledge mastery over time so as to make predictions on their future inte…
Towards the Memorization Effect of Neural Networks in Adversarial Training
Han Xu, Xiaorui Liu, Wentao Wang +5
Recent studies suggest that ``memorization'' is one important factor for overparameterized deep neural networks (DNNs) to achieve optimal performance. Specifically, the perfectly f…
Node Similarity Preserving Graph Convolutional Networks
Wei Jin, Tyler Derr, Yiqi Wang +3
Graph Neural Networks (GNNs) have achieved tremendous success in various real-world applications due to their strong ability in graph representation learning. GNNs explore the grap…
Representation Learning from Limited Educational Data with Crowdsourced Labels
Wentao Wang, Guowei Xu, Wenbiao Ding +4
Representation learning has been proven to play an important role in the unprecedented success of machine learning models in numerous tasks, such as machine translation, face recog…
Contrastive Clustering
Yunfan Li, Peng Hu, Zitao Liu +3
In this paper, we propose a one-stage online clustering method called Contrastive Clustering (CC) which explicitly performs the instance- and cluster-level contrastive learning. To…
Self-supervised Learning on Graphs: Deep Insights and New Direction
Wei Jin, Tyler Derr, Haochen Liu +4
The success of deep learning notoriously requires larger amounts of costly annotated data. This has led to the development of self-supervised learning (SSL) that aims to alleviate…
NeuCrowd: Neural Sampling Network for Representation Learning with Crowdsourced Labels
Yang Hao, Wenbiao Ding, Zitao Liu
Representation learning approaches require a massive amount of discriminative training data, which is unavailable in many scenarios, such as healthcare, smart city, education, etc.…
Graduate Employment Prediction with Bias
Teng Guo, Feng Xia, Shihao Zhen +4
The failure of landing a job for college students could cause serious social consequences such as drunkenness and suicide. In addition to academic performance, unconscious biases c…
Hierarchical Bayesian Personalized Recommendation: A Case Study and Beyond
Zitao Liu, Zhexuan Xu, Yan Yan
Items in modern recommender systems are often organized in hierarchical structures. These hierarchical structures and the data within them provide valuable information for building…
R-Transformer: Recurrent Neural Network Enhanced Transformer
Zhiwei Wang, Yao Ma, Zitao Liu +1
Recurrent Neural Networks have long been the dominating choice for sequence modeling. However, it severely suffers from two issues: impotent in capturing very long-term dependencie…