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Journal Articles
- 2024
[j7]Xiaomei Liao, Lirong He, Jiayou Mao, Meng Xu:
Spectral Superresolution Using Transformer with Convolutional Spectral Self-Attention. Remote. Sens. 16(10): 1688 (2024)- 2023
- [j6]Qingzhong Ai, Shiyu Liu, Lirong He, Zenglin Xu
:
Stein Variational Gradient Descent with Multiple Kernels. Cogn. Comput. 15(2): 672-682 (2023) - [j5]Lirong He, Qingzhong Ai, Yuqing Lei, Lili Pan, Yazhou Ren, Zenglin Xu:
Edge enhancement improves adversarial robustness in image classification. Neurocomputing 518: 122-132 (2023) - [j4]Lirong He, Qingzhong Ai, Xincheng Yang, Yazhou Ren, Qifan Wang, Zenglin Xu:
Boosting adversarial robustness via self-paced adversarial training. Neural Networks 167: 706-714 (2023) - 2021
- [j3]Liangjian Wen, Haoli Bai, Lirong He, Yiji Zhou, Mingyuan Zhou, Zenglin Xu:
Gradient estimation of information measures in deep learning. Knowl. Based Syst. 224: 107046 (2021) - 2018
- [j2]Bin Liu, Lirong He, Yingming Li, Shandian Zhe, Zenglin Xu:
NeuralCP: Bayesian Multiway Data Analysis with Neural Tensor Decomposition. Cogn. Comput. 10(6): 1051-1061 (2018) - [j1]Lirong He, Bin Liu, Guangxi Li, Yongpan Sheng, Yafang Wang, Zenglin Xu:
Knowledge Base Completion by Variational Bayesian Neural Tensor Decomposition. Cogn. Comput. 10(6): 1075-1084 (2018)
Conference and Workshop Papers
- 2023
- [c7]Qingzhong Ai, Pengyun Wang, Lirong He, Liangjian Wen, Lujia Pan, Zenglin Xu:
Generative Oversampling for Imbalanced Data via Majority-Guided VAE. AISTATS 2023: 3315-3330 - [c6]Jinze Sun, Yongpan Sheng, Ling Zhan, Lirong He:
TKGR-RHETNE: A New Temporal Knowledge Graph Reasoning Model via Jointly Modeling Relevant Historical Event and Temporal Neighborhood Event Context. ICONIP (5) 2023: 331-343 - 2021
- [c5]Qingzhong Ai, Lirong He, Shiyu Liu, Zenglin Xu:
ByPE-VAE: Bayesian Pseudocoresets Exemplar VAE. NeurIPS 2021: 5910-5920 - 2020
- [c4]Liangjian Wen, Yiji Zhou, Lirong He, Mingyuan Zhou, Zenglin Xu:
Mutual Information Gradient Estimation for Representation Learning. ICLR 2020 - 2019
- [c3]Lirong He, Ziyi Guo, Kaizhu Huang, Zenglin Xu:
Deep Minimax Probability Machine. ICDM Workshops 2019: 869-876 - 2018
- [c2]Hao Liu, Lirong He, Haoli Bai, Bo Dai, Kun Bai, Zenglin Xu:
Structured Inference for Recurrent Hidden Semi-markov Model. IJCAI 2018: 2447-2453 - 2017
- [c1]Liqiang Wang, Yafang Wang, Bin Liu, Lirong He, Shijun Liu, Gerard de Melo, Zenglin Xu:
Link prediction by exploiting network formation games in exchangeable graphs. IJCNN 2017: 619-626
Informal and Other Publications
- 2024
- [i6]Jinze Sun, Yongpan Sheng, Lirong He:
CEGRL-TKGR: A Causal Enhanced Graph Representation Learning Framework for Improving Temporal Knowledge Graph Extrapolation Reasoning. CoRR abs/2408.07911 (2024) - 2023
- [i5]Qingzhong Ai, Pengyun Wang, Lirong He, Liangjian Wen, Lujia Pan, Zenglin Xu:
Generative Oversampling for Imbalanced Data via Majority-Guided VAE. CoRR abs/2302.10910 (2023) - 2021
- [i4]Qingzhong Ai, Lirong He, Liangjian Wen, Shiyu Liu, Zenglin Xu:
ByPE-VAE: Bayesian Pseudocoresets Exemplar VAE. CoRR abs/2107.09286 (2021) - 2020
- [i3]Liangjian Wen, Yiji Zhou, Lirong He, Mingyuan Zhou, Zenglin Xu:
Mutual Information Gradient Estimation for Representation Learning. CoRR abs/2005.01123 (2020) - 2019
- [i2]Lirong He, Ziyi Guo, Kaizhu Huang, Zenglin Xu:
Deep Minimax Probability Machine. CoRR abs/1911.08723 (2019) - 2017
- [i1]Hao Liu, Haoli Bai, Lirong He, Zenglin Xu:
Stochastic Sequential Neural Networks with Structured Inference. CoRR abs/1705.08695 (2017)
Coauthor Index
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