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Longfei Li
Longfei Li
Ant Financial Service Group
Verified email at antgroup.com
Title
Cited by
Cited by
Year
Geniepath: Graph neural networks with adaptive receptive paths
Z Liu, C Chen, L Li, J Zhou, X Li, L Song, Y Qi
Proceedings of the AAAI conference on artificial intelligence 33 (01), 4424-4431, 2019
3022019
Hybrid deep neural network--hidden markov model (dnn-hmm) based speech emotion recognition
L Li, Y Zhao, D Jiang, Y Zhang, F Wang, I Gonzalez, E Valentin, H Sahli
2013 Humaine association conference on affective computing and intelligent …, 2013
2122013
Cross-domain recommendation: challenges, progress, and prospects
F Zhu, Y Wang, C Chen, J Zhou, L Li, G Liu
arXiv preprint arXiv:2103.01696, 2021
1512021
Distributed deep forest and its application to automatic detection of cash-out fraud
YL Zhang, J Zhou, W Zheng, J Feng, L Li, Z Liu, M Li, Z Zhang, C Chen, ...
ACM Transactions on Intelligent Systems and Technology (TIST) 10 (5), 1-19, 2019
822019
Anomaly detection with partially observed anomalies
YL Zhang, L Li, J Zhou, X Li, ZH Zhou
Companion Proceedings of the The Web Conference 2018, 639-646, 2018
742018
Kunpeng: Parameter server based distributed learning systems and its applications in alibaba and ant financial
J Zhou, X Li, P Zhao, C Chen, L Li, X Yang, Q Cui, J Yu, X Chen, Y Ding, ...
Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge …, 2017
502017
A unified framework for cross-domain and cross-system recommendations
F Zhu, Y Wang, J Zhou, C Chen, L Li, G Liu
IEEE Transactions on Knowledge and Data Engineering, 2021
442021
POSTER: A PU learning based system for potential malicious URL detection
YL Zhang, L Li, J Zhou, X Li, Y Liu, Y Zhang, ZH Zhou
Proceedings of the 2017 ACM SIGSAC conference on computer and communications …, 2017
422017
Multi-interactive attention network for fine-grained feature learning in ctr prediction
K Zhang, H Qian, Q Cui, Q Liu, L Li, J Zhou, J Ma, E Chen
Proceedings of the 14th ACM international conference on web search and data …, 2021
402021
Sail: Self-augmented graph contrastive learning
L Yu, S Pei, L Ding, J Zhou, L Li, C Zhang, X Zhang
Proceedings of the AAAI Conference on Artificial Intelligence 36 (8), 8927-8935, 2022
352022
Locally connected deep learning framework for industrial-scale recommender systems
C Chen, P Zhao, L Li, J Zhou, X Li, M Qiu
Proceedings of the 26th international conference on World Wide Web companion …, 2017
332017
Safe: Scalable automatic feature engineering framework for industrial tasks
Q Shi, YL Zhang, L Li, X Yang, M Li, J Zhou
2020 IEEE 36th International Conference on Data Engineering (ICDE), 1645-1656, 2020
222020
Enhancing recommender systems with large language model reasoning graphs
Y Wang, Z Chu, X Ouyang, S Wang, H Hao, Y Shen, J Gu, S Xue, ...
arXiv preprint arXiv:2308.10835, 2023
212023
Metacare++: Meta-learning with hierarchical subtyping for cold-start diagnosis prediction in healthcare data
Y Tan, C Yang, X Wei, C Chen, W Liu, L Li, J Zhou, X Zheng
Proceedings of the 45th International ACM SIGIR Conference on Research and …, 2022
192022
Leveraging large language models for pre-trained recommender systems
Z Chu, H Hao, X Ouyang, S Wang, Y Wang, Y Shen, J Gu, Q Cui, L Li, ...
arXiv preprint arXiv:2308.10837, 2023
182023
Simtrojan: Stealthy backdoor attack
Y Ren, L Li, J Zhou
2021 IEEE International Conference on Image Processing (ICIP), 819-823, 2021
162021
Prediction algorithm based attribute data processing
Y Lei, G Li, W Ding, J Huang, C Tan, S Chen, M Shi, P Zhao, L Li, Z Zhang
US Patent 11,244,402, 2022
122022
A time attention based fraud transaction detection framework
L Li, Z Liu, C Chen, YL Zhang, J Zhou, X Li
arXiv preprint arXiv:1912.11760, 2019
122019
Unleashing the power of graph data augmentation on covariate distribution shift
Y Sui, Q Wu, J Wu, Q Cui, L Li, J Zhou, X Wang, X He
Advances in Neural Information Processing Systems 36, 2024
102024
Data-centric financial large language models
Z Chu, H Guo, X Zhou, Y Wang, F Yu, H Chen, W Xu, X Lu, Q Cui, L Li, ...
arXiv preprint arXiv:2310.17784, 2023
92023
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