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Jie Lu 0001
Person information
- affiliation: University of Technology Sydney, Faculty of Engineering and Information Technology, Australia
- affiliation (PhD 2000): Curtin University, Bentley, WA, Australia
Other persons with the same name
- Jie Lu — disambiguation page
- Jie Lu 0002 — IBM Research Division, Thomas J. Watson Research Center, Yorktown Heights, NY, USA (and 1 more)
- Jie Lu 0003 — Beihang University, State Key Laboratory of Virtual Reality Technology and Systems, Beijing, China
- Jie Lu 0004 — JD.com, China (and 1 more)
- Jie Lu 0005 — KTH Royal Institute of Technology, School of Electrical Engineering, ACCESS Linnaeus Center, Sweden
- Jie Lu 0006 (aka: Lu Jie 0006) — National Digital Switching System Engineering and Technological Research and Development Center, Zhengzhou, China (and 2 more)
- Jie Lu 0007 — ShanghaiTech University, School of Information Science and Technology, China (and 1 more)
- Jie Lu 0009 — University of Chinese Academy of Sciences, State Key Laboratory of Computer Architecture, Institute of Computing Technology, Beijing, China (and 1 more)
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2020 – today
- 2024
- [j256]Ningyuan Zhang, Jie Lu, Keqiuyin Li, Zhen Fang, Guangquan Zhang:
Source-Free Unsupervised Domain Adaptation: Current research and future directions. Neurocomputing 564: 126921 (2024) - [j255]Kun Wang, Li Xiong, Anjin Liu, Guangquan Zhang, Jie Lu:
A self-adaptive ensemble for user interest drift learning. Neurocomputing 577: 127308 (2024) - [j254]Haotian Xu, Junyu Xuan, Guangquan Zhang, Jie Lu:
Trust region policy optimization via entropy regularization for Kullback-Leibler divergence constraint. Neurocomputing 589: 127716 (2024) - [j253]Keqiuyin Li, Jie Lu, Hua Zuo, Guangquan Zhang:
Multi-source domain adaptation handling inaccurate label spaces. Neurocomputing 594: 127824 (2024) - [j252]Zhaoqing Liu, Guangquan Zhang, Jie Lu:
Semi-supervised heterogeneous domain adaptation for few-sample credit risk classification. Neurocomputing 596: 127948 (2024) - [j251]Zhen Fang, Yixuan Li, Feng Liu, Bo Han, Jie Lu:
On the Learnability of Out-of-distribution Detection. J. Mach. Learn. Res. 25: 84:1-84:83 (2024) - [j250]Tianyu Liu, Jie Lu, Zheng Yan, Guangquan Zhang:
Robust Gaussian Process Regression With Input Uncertainty: A PAC-Bayes Perspective. IEEE Trans. Cybern. 54(2): 962-973 (2024) - [j249]Guangzhi Ma, Jie Lu, Feng Liu, Zhen Fang, Guangquan Zhang:
Multiclass Classification With Fuzzy-Feature Observations: Theory and Algorithms. IEEE Trans. Cybern. 54(2): 1048-1061 (2024) - [j248]Zhen Fang, Jie Lu, Guangquan Zhang:
An Extremely Simple Algorithm for Source Domain Reconstruction. IEEE Trans. Cybern. 54(3): 1921-1933 (2024) - [j247]Keqiuyin Li, Jie Lu, Hua Zuo, Guangquan Zhang:
Multidomain Adaptation With Sample and Source Distillation. IEEE Trans. Cybern. 54(4): 2193-2205 (2024) - [j246]Zihe Liu, Jie Lu, Junyu Xuan, Guangquan Zhang:
Deep Reinforcement Learning in Nonstationary Environments With Unknown Change Points. IEEE Trans. Cybern. 54(9): 5191-5204 (2024) - [j245]Kun Wang, Jie Lu, Anjin Liu, Guangquan Zhang:
TS-DM: A Time Segmentation-Based Data Stream Learning Method for Concept Drift Adaptation. IEEE Trans. Cybern. 54(10): 6000-6011 (2024) - [j244]Guangzhi Ma, Jie Lu, Guangquan Zhang:
Multisource Domain Adaptation With Interval-Valued Target Data via Fuzzy Neural Networks. IEEE Trans. Fuzzy Syst. 32(5): 3094-3106 (2024) - [j243]Guangzhi Ma, Jie Lu, Feng Liu, Zhen Fang, Guangquan Zhang:
Domain Adaptation With Interval-Valued Observations: Theory and Algorithms. IEEE Trans. Fuzzy Syst. 32(5): 3107-3120 (2024) - [j242]Jie Lu, Guangzhi Ma, Guangquan Zhang:
Fuzzy Machine Learning: A Comprehensive Framework and Systematic Review. IEEE Trans. Fuzzy Syst. 32(7): 3861-3878 (2024) - [j241]Kuo Shi, Jie Lu, Zhen Fang, Guangquan Zhang:
Unsupervised Domain Adaptation Enhanced by Fuzzy Prompt Learning. IEEE Trans. Fuzzy Syst. 32(7): 4038-4048 (2024) - [j240]En Yu, Jie Lu, Guangquan Zhang:
Fuzzy Shared Representation Learning for Multistream Classification. IEEE Trans. Fuzzy Syst. 32(10): 5625-5637 (2024) - [j239]Kezhi Lu, Qian Zhang, Danny Hughes, Guangquan Zhang, Jie Lu:
AMT-CDR: A Deep Adversarial Multi-Channel Transfer Network for Cross-Domain Recommendation. ACM Trans. Intell. Syst. Technol. 15(4): 87:1-87:26 (2024) - [j238]Mohammad Siami, Mohsen Naderpour, Fahimeh Ramezani, Jie Lu:
Risk Assessment Through Big Data: An Autonomous Fuzzy Decision Support System. IEEE Trans. Intell. Transp. Syst. 25(8): 9016-9027 (2024) - [j237]Ming Zhou, Jie Lu, Pengqian Lu, Guangquan Zhang:
Dynamic Graph Regularization for Multi-Stream Concept Drift Self-Adaptation. IEEE Trans. Knowl. Data Eng. 36(11): 6016-6028 (2024) - [j236]Wei Duan, Jie Lu, Yu Guang Wang, Junyu Xuan:
Layer-diverse Negative Sampling for Graph Neural Networks. Trans. Mach. Learn. Res. 2024 (2024) - [j235]Ruxia Liang, Qian Zhang, Jianqiang Wang, Jie Lu:
A Hierarchical Attention Network for Cross-Domain Group Recommendation. IEEE Trans. Neural Networks Learn. Syst. 35(3): 3859-3873 (2024) - [c228]En Yu, Jie Lu, Bin Zhang, Guangquan Zhang:
Online Boosting Adaptive Learning under Concept Drift for Multistream Classification. AAAI 2024: 16522-16530 - [c227]Kuo Shi, Jie Lu, Zhen Fang, Guangquan Zhang:
Enhancing Vision-Language Models Incorporating TSK Fuzzy System for Domain Adaptation. FUZZ 2024: 1-8 - [c226]Bo Peng, Zhen Fang, Guangquan Zhang, Jie Lu:
Knowledge Distillation with Auxiliary Variable. ICML 2024 - [c225]Wei Duan, Jie Lu, Junyu Xuan:
Group-Aware Coordination Graph for Multi-Agent Reinforcement Learning. IJCAI 2024: 3926-3934 - [c224]Zihe Liu, Jie Lu, Guangquan Zhang, Junyu Xuan:
A Behavior-Aware Approach for Deep Reinforcement Learning in Non-stationary Environments without Known Change Points. IJCAI 2024: 4634-4642 - [c223]Kuo Shi, Jie Lu, Zhen Fang, Guangquan Zhang:
CLIP-Enhanced Unsupervised Domain Adaptation with Consistency Regularization. IJCNN 2024: 1-8 - [c222]Ran Wang, Hua Zuo, Zhen Fang, Jie Lu:
Prompt-Based Memory Bank for Continual Test-Time Domain Adaptation in Vision-Language Models. IJCNN 2024: 1-8 - [i44]Wei Duan, Jie Lu, Yu Guang Wang, Junyu Xuan:
Layer-diverse Negative Sampling for Graph Neural Networks. CoRR abs/2403.11408 (2024) - [i43]Wei Duan, Jie Lu, Junyu Xuan:
Inferring Latent Temporal Sparse Coordination Graph for Multi-Agent Reinforcement Learning. CoRR abs/2403.19253 (2024) - [i42]Zhen Fang, Yixuan Li, Feng Liu, Bo Han, Jie Lu:
On the Learnability of Out-of-distribution Detection. CoRR abs/2404.04865 (2024) - [i41]Wei Duan, Jie Lu, Junyu Xuan:
Group-Aware Coordination Graph for Multi-Agent Reinforcement Learning. CoRR abs/2404.10976 (2024) - [i40]Yi Zhang, Mengjia Wu, Guangquan Zhang, Jie Lu:
Responsible AI: Portraits with Intelligent Bibliometrics. CoRR abs/2405.02846 (2024) - [i39]Feng Gu, Jie Lu, Zhen Fang, Kun Wang, Guangquan Zhang:
A Neighbor-Searching Discrepancy-based Drift Detection Scheme for Learning Evolving Data. CoRR abs/2405.14153 (2024) - [i38]Zihe Liu, Jie Lu, Guangquan Zhang, Junyu Xuan:
A Behavior-Aware Approach for Deep Reinforcement Learning in Non-stationary Environments without Known Change Points. CoRR abs/2405.14214 (2024) - [i37]Guanjin Wang, Junyu Xuan, Penghao Wang, Chengdao Li, Jie Lu:
LSTM Autoencoder-based Deep Neural Networks for Barley Genotype-to-Phenotype Prediction. CoRR abs/2407.16709 (2024) - [i36]Guohang Zeng, Qian Zhang, Guangquan Zhang, Jie Lu:
Sharpness-Aware Cross-Domain Recommendation to Cold-Start Users. CoRR abs/2408.01931 (2024) - 2023
- [j234]Suyue Han, Bin Liu, Xinyue Fan, Tingting Feng, JingJing Yang, Zhongli Zhou, Hao Gong, Jie Lu:
A new approach for landslide susceptibility assessments based on KDE-MDBN: A case study from mountainous regions impacted by the Wenchuan earthquake, China. Environ. Model. Softw. 167: 105759 (2023) - [j233]Haotian Xu, Zheng Yan, Junyu Xuan, Guangquan Zhang, Jie Lu:
Improving proximal policy optimization with alpha divergence. Neurocomputing 534: 94-105 (2023) - [j232]Yi Zhang, Mengjia Wu, Guangquan Zhang, Jie Lu:
Stepping beyond your comfort zone: Diffusion-based network analytics for knowledge trajectory recommendation. J. Assoc. Inf. Sci. Technol. 74(7): 775-790 (2023) - [j231]Kairui Guo, Mengjia Wu, Zelia Soo, Yue Yang, Yi Zhang, Qian Zhang, Hua Lin, Mark Grosser, Deon Venter, Guangquan Zhang, Jie Lu:
Artificial intelligence-driven biomedical genomics. Knowl. Based Syst. 279: 110937 (2023) - [j230]Zhen Fang, Jie Lu, Feng Liu, Guangquan Zhang:
Semi-Supervised Heterogeneous Domain Adaptation: Theory and Algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 45(1): 1087-1105 (2023) - [j229]Hang Yu, Weixu Liu, Jie Lu, Yimin Wen, Xiangfeng Luo, Guangquan Zhang:
Detecting group concept drift from multiple data streams. Pattern Recognit. 134: 109113 (2023) - [j228]Kun Wang, Jie Lu, Anjin Liu, Guangquan Zhang, Li Xiong:
Evolving Gradient Boost: A Pruning Scheme Based on Loss Improvement Ratio for Learning Under Concept Drift. IEEE Trans. Cybern. 53(4): 2110-2123 (2023) - [j227]Keqiuyin Li, Jie Lu, Hua Zuo, Guangquan Zhang:
Source-Free Multidomain Adaptation With Fuzzy Rule-Based Deep Neural Networks. IEEE Trans. Fuzzy Syst. 31(12): 4180-4194 (2023) - [j226]Fujin Zhu, Jie Lu, Adi Lin, Junyu Xuan, Guangquan Zhang:
Direct Learning With Multi-Task Neural Networks for Treatment Effect Estimation. IEEE Trans. Knowl. Data Eng. 35(3): 2457-2470 (2023) - [j225]Qian Zhang, Wenhui Liao, Guangquan Zhang, Bo Yuan, Jie Lu:
A Deep Dual Adversarial Network for Cross-Domain Recommendation. IEEE Trans. Knowl. Data Eng. 35(4): 3266-3278 (2023) - [j224]Anjin Liu, Jie Lu, Yiliao Song, Junyu Xuan, Guangquan Zhang:
Concept Drift Detection Delay Index. IEEE Trans. Knowl. Data Eng. 35(5): 4585-4597 (2023) - [j223]Keqiuyin Li, Jie Lu, Hua Zuo, Guangquan Zhang:
Dynamic Classifier Alignment for Unsupervised Multi-Source Domain Adaptation. IEEE Trans. Knowl. Data Eng. 35(5): 4727-4740 (2023) - [j222]Ming Zhou, Jie Lu, Yiliao Song, Guangquan Zhang:
Multi-Stream Concept Drift Self-Adaptation Using Graph Neural Network. IEEE Trans. Knowl. Data Eng. 35(12): 12828-12841 (2023) - [j221]Qian Liu, Xiubo Geng, Heyan Huang, Tao Qin, Jie Lu, Daxin Jiang:
MGRC: An End-to-End Multigranularity Reading Comprehension Model for Question Answering. IEEE Trans. Neural Networks Learn. Syst. 34(5): 2594-2605 (2023) - [j220]Li Zhong, Zhen Fang, Feng Liu, Bo Yuan, Guangquan Zhang, Jie Lu:
Bridging the Theoretical Bound and Deep Algorithms for Open Set Domain Adaptation. IEEE Trans. Neural Networks Learn. Syst. 34(8): 3859-3873 (2023) - [j219]Yiliao Song, Jie Lu, Haiyan Lu, Guangquan Zhang:
Learning Data Streams With Changing Distributions and Temporal Dependency. IEEE Trans. Neural Networks Learn. Syst. 34(8): 3952-3965 (2023) - [j218]Jie Lu, João Gama, Xin Yao, Leandro L. Minku:
Guest Editorial: Special Issue on Stream Learning. IEEE Trans. Neural Networks Learn. Syst. 34(10): 6683-6685 (2023) - [j217]Wenhui Liao, Qian Zhang, Bo Yuan, Guangquan Zhang, Jie Lu:
Heterogeneous Multidomain Recommender System Through Adversarial Learning. IEEE Trans. Neural Networks Learn. Syst. 34(11): 8965-8977 (2023) - [c221]Ran Wang, Hua Zuo, Zhen Fang, Jie Lu:
Multiple Teacher Model for Continual Test-Time Domain Adaptation. AI (1) 2023: 304-314 - [c220]Kun Wang, Jie Lu, Anjin Liu, Guangquan Zhang:
An Augmented Learning Approach for Multiple Data Streams Under Concept Drift. AI (1) 2023: 391-402 - [c219]Shanshan Ye, Jie Lu:
Sequence Unlearning for Sequential Recommender Systems. AI (1) 2023: 403-415 - [c218]Keqiuyin Li, Jie Lu, Hua Zuo, Guangquan Zhang:
Attention-Bridging TS Fuzzy Rules for Universal Multi-Domain Adaptation Without Source Data. FUZZ 2023: 1-6 - [c217]Guangzhi Ma, Jie Lu, Guangquan Zhang:
Interval-Valued Observations-Based Multi-Source Domain Adaptation Using Fuzzy Neural Networks. FUZZ 2023: 1-6 - [c216]Mengjia Wu, Yi Zhang, Hua Lin, Mark Grosser, Guangquan Zhang, Jie Lu:
BiblioEngine: An AI-Empowered Platform for Disease Genetic Knowledge Mining. HIS 2023: 187-198 - [c215]Yue Yang, Kairui Guo, Zhen Fang, Hua Lin, Mark Grosser, Jie Lu:
Multi-model Transfer Learning and Genotypic Analysis for Seizure Type Classification. HIS 2023: 223-234 - [c214]Xinheng Wu, Jie Lu, Zhen Fang, Guangquan Zhang:
Meta OOD Learning For Continuously Adaptive OOD Detection. ICCV 2023: 19296-19307 - [c213]Guohang Zeng, Zhen Fang, Guangquan Zhang, Jie Lu:
One-step Domain Adaptation Approach with Partial Label. IJCNN 2023: 1-8 - [c212]Zelia Soo, Hua Lin, Yue Yang, Mark Grosser, Yi Zhang, Jie Lu:
Deep Neural Network-Empowered Polygenic Disease Prediction on Cardiovascular Diseases. ISKE 2023: 309-315 - [c211]Keqiuyin Li, Jie Lu, Hua Zuo, Guangquan Zhang:
Domain Adaptation for Image Segmentation with Category-Guide Classifier. ISKE 2023: 568-572 - [c210]Keqiuyin Li, Jie Lu, Hua Zuo, Guangquan Zhang:
Multi-Source Domain Adaptation with Incomplete Source Label Spaces. KES 2023: 2343-2350 - [c209]Pham Minh Thu Do, Qian Zhang, Guangquan Zhang, Jie Lu:
meta-GRS: A Graph Neural Network for Cross-Domain Recommender System via Meta-Learning. KES 2023: 2536-2545 - [c208]Kun Wang, Jie Lu, Anjin Liu, Guangquan Zhang:
TCR-M: A Topic Change Recognition-based Method for Data Stream Learning. KES 2023: 3001-3010 - [i35]Xinheng Wu, Jie Lu, Zhen Fang, Guangquan Zhang:
Meta OOD Learning for Continuously Adaptive OOD Detection. CoRR abs/2309.11705 (2023) - [i34]En Yu, Jie Lu, Bin Zhang, Guangquan Zhang:
Online Boosting Adaptive Learning under Concept Drift for Multistream Classification. CoRR abs/2312.10841 (2023) - 2022
- [j216]Bin Wang, Jie Lu, Tianrui Li, Zheng Yan, Guangquan Zhang:
A quantile fusion methodology for deep forecasting. Neurocomputing 483: 286-298 (2022) - [j215]Kun Wang, Jie Lu, Anjin Liu, Yiliao Song, Li Xiong, Guangquan Zhang:
Elastic gradient boosting decision tree with adaptive iterations for concept drift adaptation. Neurocomputing 491: 288-304 (2022) - [j214]En Yu, Yiliao Song, Guangquan Zhang, Jie Lu:
Learn-to-adapt: Concept drift adaptation for hybrid multiple streams. Neurocomputing 496: 121-130 (2022) - [j213]Kai Yang, Jie Lu, Wanggen Wan, Guangquan Zhang, Li Hou:
Transfer learning based on sparse Gaussian process for regression. Inf. Sci. 605: 286-300 (2022) - [j212]Hang Yu, Qingyong Zhang, Tianyu Liu, Jie Lu, Yimin Wen, Guangquan Zhang:
Meta-ADD: A meta-learning based pre-trained model for concept drift active detection. Inf. Sci. 608: 996-1009 (2022) - [j211]Di Zhang, Zhongli Zhou, Suyue Han, Hao Gong, Tianyi Zou, Jie Lu:
Deep Metallogenic prediction model construction of the Xiongcun no. II orebody based on the DNN algorithm. Multim. Tools Appl. 81(23): 33185-33203 (2022) - [j210]Guanjin Wang, Kup-Sze Choi, Jeremy Yuen-Chun Teoh, Jie Lu:
Deep Cross-Output Knowledge Transfer Using Stacked-Structure Least-Squares Support Vector Machines. IEEE Trans. Cybern. 52(5): 3207-3220 (2022) - [j209]Hang Yu, Jie Lu, Guangquan Zhang:
Continuous Support Vector Regression for Nonstationary Streaming Data. IEEE Trans. Cybern. 52(5): 3592-3605 (2022) - [j208]Guanjin Wang, Ta Zhou, Kup-Sze Choi, Jie Lu:
A Deep-Ensemble-Level-Based Interpretable Takagi-Sugeno-Kang Fuzzy Classifier for Imbalanced Data. IEEE Trans. Cybern. 52(5): 3805-3818 (2022) - [j207]Fan Dong, Jie Lu, Yiliao Song, Feng Liu, Guangquan Zhang:
A Drift Region-Based Data Sample Filtering Method. IEEE Trans. Cybern. 52(9): 9377-9390 (2022) - [j206]Hang Yu, Jie Lu, Guangquan Zhang:
Topology Learning-Based Fuzzy Random Neural Networks for Streaming Data Regression. IEEE Trans. Fuzzy Syst. 30(2): 412-425 (2022) - [j205]Xiaoya Che, Hua Zuo, Jie Lu, Degang Chen:
Fuzzy Multioutput Transfer Learning for Regression. IEEE Trans. Fuzzy Syst. 30(7): 2438-2451 (2022) - [j204]Hang Yu, Jie Lu, Anjin Liu, Bin Wang, Ruimin Li, Guangquan Zhang:
Real-Time Prediction System of Train Carriage Load Based on Multi-Stream Fuzzy Learning. IEEE Trans. Intell. Transp. Syst. 23(9): 15155-15165 (2022) - [j203]Hang Yu, Jie Lu, Guangquan Zhang:
An Online Robust Support Vector Regression for Data Streams. IEEE Trans. Knowl. Data Eng. 34(1): 150-163 (2022) - [j202]Yiliao Song, Jie Lu, Anjin Liu, Haiyan Lu, Guangquan Zhang:
A Segment-Based Drift Adaptation Method for Data Streams. IEEE Trans. Neural Networks Learn. Syst. 33(9): 4876-4889 (2022) - [j201]Keqiuyin Li, Jie Lu, Hua Zuo, Guangquan Zhang:
Multi-Source Contribution Learning for Domain Adaptation. IEEE Trans. Neural Networks Learn. Syst. 33(10): 5293-5307 (2022) - [j200]Yiyang Zhang, Feng Liu, Zhen Fang, Bo Yuan, Guangquan Zhang, Jie Lu:
Learning From a Complementary-Label Source Domain: Theory and Algorithms. IEEE Trans. Neural Networks Learn. Syst. 33(12): 7667-7681 (2022) - [j199]Hang Yu, Jie Lu, Guangquan Zhang:
MORStreaming: A Multioutput Regression System for Streaming Data. IEEE Trans. Syst. Man Cybern. Syst. 52(8): 4862-4874 (2022) - [c207]Wei Duan, Junyu Xuan, Maoying Qiao, Jie Lu:
Learning from the Dark: Boosting Graph Convolutional Neural Networks with Diverse Negative Samples. AAAI 2022: 6550-6558 - [c206]Zhaoqing Liu, Anjin Liu, Guangquan Zhang, Jie Lu:
An Empirical Study of Fuzzy Decision Tree for Gradient Boosting Ensemble. AI 2022: 716-727 - [c205]Jie Lu:
Machine Learning for Decision Support in Complex Environments. COMPLEXIS 2022: 9 - [c204]Keqiuyin Li, Jie Lu, Hua Zuo, Guangquan Zhang:
Source-Free Multi-Domain Adaptation with Generally Auxiliary Model Training. IJCNN 2022: 1-8 - [c203]Jie Lu:
Machine Learning for Decision Support in Complex Environments. IoTBDS 2022: 5 - [c202]Zhen Fang, Yixuan Li, Jie Lu, Jiahua Dong, Bo Han, Feng Liu:
Is Out-of-Distribution Detection Learnable? NeurIPS 2022 - [i33]Yi Zhang, Mengjia Wu, Jie Lu:
Stepping beyond your comfort zone: Diffusion-based network analytics for knowledge trajectory recommendation. CoRR abs/2205.15504 (2022) - [i32]Guangzhi Ma, Jie Lu, Feng Liu, Zhen Fang, Guangquan Zhang:
Multi-class Classification with Fuzzy-feature Observations: Theory and Algorithms. CoRR abs/2206.04311 (2022) - [i31]Wei Duan, Junyu Xuan, Maoying Qiao, Jie Lu:
Learning from the Dark: Boosting Graph Convolutional Neural Networks with Diverse Negative Samples. CoRR abs/2210.00728 (2022) - [i30]Tianyu Liu, Jie Lu, Zheng Yan, Guangquan Zhang:
Streaming PAC-Bayes Gaussian process regression with a performance guarantee for online decision making. CoRR abs/2210.08486 (2022) - [i29]Zhen Fang, Yixuan Li, Jie Lu, Jiahua Dong, Bo Han, Feng Liu:
Is Out-of-Distribution Detection Learnable? CoRR abs/2210.14707 (2022) - [i28]Wei Duan, Junyu Xuan, Maoying Qiao, Jie Lu:
Graph Convolutional Neural Networks with Diverse Negative Samples via Decomposed Determinant Point Processes. CoRR abs/2212.02055 (2022) - 2021
- [j198]Wenjun Chang, Qian Zhang, Chao Fu, Weiyong Liu, Guangquan Zhang, Jie Lu:
A cross-domain recommender system through information transfer for medical diagnosis. Decis. Support Syst. 143: 113489 (2021) - [j197]Mengjia Wu, Yi Zhang, Mark Grosser, Steven Tipper, Deon Venter, Hua Lin, Jie Lu:
Profiling COVID-19 Genetic Research: A Data-Driven Study Utilizing Intelligent Bibliometrics. Frontiers Res. Metrics Anal. 6: 683212 (2021) - [j196]Nur Atiqah Rochin Demong, Jie Lu, Farookh Khadeer Hussain:
An Adaptive Personalized Property Investment Risk Analysis Method Based on Data-Driven Approach. Int. J. Inf. Technol. Decis. Mak. 20(2): 671-706 (2021) - [j195]Tianyu Liu, Jie Lu, Zheng Yan, Guangquan Zhang:
Statistical generalization performance guarantee for meta-learning with data dependent prior. Neurocomputing 465: 391-405 (2021) - [j194]Qian Liu, Jie Lu, Guangquan Zhang, Tao Shen, Zhihan Zhang, Heyan Huang:
Domain-specific meta-embedding with latent semantic structures. Inf. Sci. 555: 410-423 (2021) - [j193]Kai Yang, Jie Lu, Wanggen Wan, Guangquan Zhang:
Multi-source transfer regression via source-target pairwise segment. Inf. Sci. 556: 389-403 (2021) - [j192]Yi Zhang, Mengjia Wu, Wen Miao, Lu Huang, Jie Lu:
Bi-layer network analytics: A methodology for characterizing emerging general-purpose technologies. J. Informetrics 15(4): 101202 (2021) - [j191]