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2020 – today
- 2024
[j7]Liangliang He
, Xiao Li, Pancheng Wang, Jintao Tang, Ting Wang:
MAN: Memory-augmented Attentive Networks for Deep Learning-based Knowledge Tracing. ACM Trans. Inf. Syst. 42(1): 8:1-8:22 (2024)- [c10]Liangliang He, Xiutang Geng, Peng Zhang, Longqiang Ni, Chenguang Li, Kun Wang:
Multi-objective Optimization of Stereoscopic Shelf Space Allocation Based on Adaptive Non-repetitive Genetic Algorithm. CMLDS 2024: 59:1-59:6 - 2023
- [j6]Wei Yu, Qingsong Yin, Hao Yin, Wei Xiao, Tao Chang, Liangliang He, Lulin Ni, Qingbing Ji:
A Systematic Review on Password Guessing Tasks. Entropy 25(9): 1303 (2023) - [j5]Hongyan Wang, WeiZhen Wu, Zhi Dou, Liangliang He, Liqiang Yang:
Performance and exploration of ChatGPT in medical examination, records and education in Chinese: Pave the way for medical AI. Int. J. Medical Informatics 177: 105173 (2023) - [j4]Liangliang He, Xiao Li, Pancheng Wang, Jintao Tang, Ting Wang:
Integrating fine-grained attention into multi-task learning for knowledge tracing. World Wide Web (WWW) 26(5): 3347-3372 (2023) - [c9]Dong Li, Pancheng Wang, Liangliang He, Kunyuan Pang, Shasha Li, Jintao Tang, Ting Wang:
Distinguishing Sensitive and Insensitive Options for the Winograd Schema Challenge. DASFAA (3) 2023: 716-726 - 2022
- [j3]Liangliang He, Jintao Tang, Xiao Li, Pancheng Wang, Feng Chen, Ting Wang:
Multi-type factors representation learning for deep learning-based knowledge tracing. World Wide Web 25(3): 1343-1372 (2022) - [c8]Pancheng Wang, Shasha Li, Kunyuan Pang, Liangliang He, Dong Li, Jintao Tang, Ting Wang:
Multi-Document Scientific Summarization from a Knowledge Graph-Centric View. COLING 2022: 6222-6233 - [i1]Pancheng Wang, Shasha Li, Kunyuan Pang, Liangliang He, Dong Li, Jintao Tang, Ting Wang:
Multi-Document Scientific Summarization from a Knowledge Graph-Centric View. CoRR abs/2209.04319 (2022) - 2021
- [c7]Liangliang He, Xiao Li, Jintao Tang, Ting Wang:
EDKT: An Extensible Deep Knowledge Tracing Model for Multiple Learning Factors. DASFAA (1) 2021: 340-355 - [c6]Liangliang He:
Integrating Performance and Side Factors into Embeddings for Deep Learning-Based Knowledge Tracing. ICME 2021: 1-6 - 2020
- [c5]Liangliang He, Jintao Tang, Xiao Li, Ting Wang:
ADKT: Adaptive Deep Knowledge Tracing. WISE (1) 2020: 302-314
2010 – 2019
- 2019
- [c4]Zhenhua Tan, Danke Wu, Liangliang He, Qiuyun Chang, Bin Zhang:
SDP: An Improved Baseline Estimation Model Based On Standard Deviation Proportion. ICME 2019: 25-30 - 2018
- [j2]Xiaojuan Ban, Xiaokun Wang, Liangliang He, Yalan Zhang, LiPeng Wang:
Adaptively stepped SPH for fluid animation based on asynchronous time integration. Neural Comput. Appl. 29(1): 33-42 (2018) - [c3]Liangliang He, Zhenhua Tan, Guibing Guo, Qiuyun Chang, Danke Wu:
LFSF: Latent Factor-Based Similarity Framework and Its Application for Collaborative Recommendation. PCM (1) 2018: 744-754 - 2017
- [j1]Zhenhua Tan, Liangliang He:
An Efficient Similarity Measure for User-Based Collaborative Filtering Recommender Systems Inspired by the Physical Resonance Principle. IEEE Access 5: 27211-27228 (2017) - 2016
- [c2]Zhenhua Tan, Liangliang He, Hong Li, Xingwei Wang:
Rating Personalization Improves Accuracy: A Proportion-Based Baseline Estimate Model for Collaborative Recommendation. CollaborateCom 2016: 104-114 - 2015
- [c1]Liangliang He, Xiaojuan Ban, Xu Liu, Xiaokun Wang:
Individual Time Stepping for SPH Fluids. Eurographics (Short Papers) 2015: 41-44
Coauthor Index
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