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Guodong Long
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Publications
- 2024
- [c108]Yang Li, Canran Xu, Guodong Long, Tao Shen, Chongyang Tao, Jing Jiang:
CCPrefix: Counterfactual Contrastive Prefix-Tuning for Many-Class Classification. EACL (1) 2024: 2977-2988 - [i90]Yiyuan Yang, Guodong Long, Tao Shen, Jing Jiang, Michael Blumenstein:
Dual-Personalizing Adapter for Federated Foundation Models. CoRR abs/2403.19211 (2024) - 2023
- [j31]Chao Yang, Xianzhi Wang, Lina Yao, Guodong Long, Jing Jiang, Guandong Xu:
Attentional Gated Res2Net for Multivariate Time Series Classification. Neural Process. Lett. 55(2): 1371-1395 (2023) - [j30]Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, Chengqi Zhang:
Beyond Low-Pass Filtering: Graph Convolutional Networks With Automatic Filtering. IEEE Trans. Knowl. Data Eng. 35(7): 6687-6697 (2023) - [j29]Guodong Long, Ming Xie, Tao Shen, Tianyi Zhou, Xianzhi Wang, Jing Jiang:
Multi-center federated learning: clients clustering for better personalization. World Wide Web (WWW) 26(1): 481-500 (2023) - [c107]Yue Tan, Yixin Liu, Guodong Long, Jing Jiang, Qinghua Lu, Chengqi Zhang:
Federated Learning on Non-IID Graphs via Structural Knowledge Sharing. AAAI 2023: 9953-9961 - [c94]Yijun Yang, Tianyi Zhou, Jing Jiang, Guodong Long, Yuhui Shi:
Continual Task Allocation in Meta-Policy Network via Sparse Prompting. ICML 2023: 39623-39638 - [c93]Haiyan Zhao, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang:
Does Continual Learning Equally Forget All Parameters? ICML 2023: 42280-42303 - [c92]Shengchao Chen, Guodong Long, Tao Shen, Jing Jiang:
Prompt Federated Learning for Weather Forecasting: Toward Foundation Models on Meteorological Data. IJCAI 2023: 3532-3540 - [c89]Jie Ma, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang:
Structured Federated Learning through Clustered Additive Modeling. NeurIPS 2023 - [c87]Haiyan Zhao, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang:
Voting from Nearest Tasks: Meta-Vote Pruning of Pre-trained Models for Downstream Tasks. ECML/PKDD (2) 2023: 52-68 - [i86]Shengchao Chen, Guodong Long, Tao Shen, Jing Jiang:
Prompt Federated Learning for Weather Forecasting: Toward Foundation Models on Meteorological Data. CoRR abs/2301.09152 (2023) - [i82]Haiyan Zhao, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang:
Voting from Nearest Tasks: Meta-Vote Pruning of Pre-trained Models for Downstream Tasks. CoRR abs/2301.11560 (2023) - [i80]Haiyan Zhao, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang:
Does Continual Learning Equally Forget All Parameters? CoRR abs/2304.04158 (2023) - [i75]Shengchao Chen, Guodong Long, Tao Shen, Tianyi Zhou, Jing Jiang:
Spatial-temporal Prompt Learning for Federated Weather Forecasting. CoRR abs/2305.14244 (2023) - [i74]Yijun Yang, Tianyi Zhou, Jing Jiang, Guodong Long, Yuhui Shi:
Continual Task Allocation in Meta-Policy Network via Sparse Prompting. CoRR abs/2305.18444 (2023) - [i71]Zhihong Deng, Jing Jiang, Guodong Long, Chengqi Zhang:
Causal Reinforcement Learning: A Survey. CoRR abs/2307.01452 (2023) - [i68]Shuang Ao, Tianyi Zhou, Guodong Long, Xuan Song, Jing Jiang:
Curriculum Reinforcement Learning via Morphology-Environment Co-Evolution. CoRR abs/2309.12529 (2023) - [i66]Shengchao Chen, Guodong Long, Jing Jiang, Dikai Liu, Chengqi Zhang:
Foundation Models for Weather and Climate Data Understanding: A Comprehensive Survey. CoRR abs/2312.03014 (2023) - 2022
- [j27]Lu Liu, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang:
Many-Class Few-Shot Learning on Multi-Granularity Class Hierarchy. IEEE Trans. Knowl. Data Eng. 34(5): 2293-2305 (2022) - [j26]Zhuowei Wang, Jing Jiang, Bo Han, Lei Feng, Bo An, Gang Niu, Guodong Long:
SemiNLL: A Framework of Noisy-Label Learning by Semi-Supervised Learning. Trans. Mach. Learn. Res. 2022 (2022) - [j25]Haiyan Zhao, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang:
Extracting Local Reasoning Chains of Deep Neural Networks. Trans. Mach. Learn. Res. 2022 (2022) - [c86]Yue Tan, Guodong Long, Lu Liu, Tianyi Zhou, Qinghua Lu, Jing Jiang, Chengqi Zhang:
FedProto: Federated Prototype Learning across Heterogeneous Clients. AAAI 2022: 8432-8440 - [c81]Chao Yang, Xianzhi Wang, Lina Yao, Guodong Long, Jing Jiang, Guandong Xu:
Attentional Gated Res2net for Multivariate Time Series Classification. ICASSP 2022: 3308-3312 - [c80]Zhuowei Wang, Jing Jiang, Guodong Long:
Positive Unlabeled Learning by Semi-Supervised Learning. ICIP 2022: 2976-2980 - [c79]Wensi Tang, Guodong Long, Lu Liu, Tianyi Zhou, Michael Blumenstein, Jing Jiang:
Omni-Scale CNNs: a simple and effective kernel size configuration for time series classification. ICLR 2022 - [c78]Shuang Ao, Tianyi Zhou, Jing Jiang, Guodong Long, Xuan Song, Chengqi Zhang:
EAT-C: Environment-Adversarial sub-Task Curriculum for Efficient Reinforcement Learning. ICML 2022: 822-843 - [c76]Fengwen Chen, Guodong Long, Zonghan Wu, Tianyi Zhou, Jing Jiang:
Personalized Federated Learning With a Graph. IJCAI 2022: 2575-2582 - [c75]Yang Li, Guodong Long, Tao Shen, Jing Jiang:
Hierarchical Relation-Guided Type-Sentence Alignment for Long-Tail Relation Extraction with Distant Supervision. NAACL-HLT (Findings) 2022: 316-326 - [c73]Yue Tan, Guodong Long, Jie Ma, Lu Liu, Tianyi Zhou, Jing Jiang:
Federated Learning from Pre-Trained Models: A Contrastive Learning Approach. NeurIPS 2022 - [e3]Bohan Li, Lin Yue, Jing Jiang, Weitong Chen, Xue Li, Guodong Long, Fei Fang, Han Yu:
Advanced Data Mining and Applications - 17th International Conference, ADMA 2021, Sydney, NSW, Australia, February 2-4, 2022, Proceedings, Part I. Lecture Notes in Computer Science 13087, Springer 2022, ISBN 978-3-030-95404-8 [contents] - [e2]Bohan Li, Lin Yue, Jing Jiang, Weitong Chen, Xue Li, Guodong Long, Fei Fang, Han Yu:
Advanced Data Mining and Applications - 17th International Conference, ADMA 2021, Sydney, NSW, Australia, February 2-4, 2022, Proceedings, Part II. Lecture Notes in Computer Science 13088, Springer 2022, ISBN 978-3-030-95407-9 [contents] - [i65]Jie Ma, Guodong Long, Tianyi Zhou, Jing Jiang, Chengqi Zhang:
On the Convergence of Clustered Federated Learning. CoRR abs/2202.06187 (2022) - [i64]Fengwen Chen, Guodong Long, Zonghan Wu, Tianyi Zhou, Jing Jiang:
Personalized Federated Learning With Structure. CoRR abs/2203.00829 (2022) - [i62]Zhuowei Wang, Tianyi Zhou, Guodong Long, Bo Han, Jing Jiang:
FedNoiL: A Simple Two-Level Sampling Method for Federated Learning with Noisy Labels. CoRR abs/2205.10110 (2022) - [i60]Yue Tan, Guodong Long, Jie Ma, Lu Liu, Tianyi Zhou, Jing Jiang:
Federated Learning from Pre-Trained Models: A Contrastive Learning Approach. CoRR abs/2209.10083 (2022) - [i58]Yang Li, Canran Xu, Tao Shen, Jing Jiang, Guodong Long:
CCPrompt: Counterfactual Contrastive Prompt-Tuning for Many-Class Classification. CoRR abs/2211.05987 (2022) - [i57]Yue Tan, Yixin Liu, Guodong Long, Jing Jiang, Qinghua Lu, Chengqi Zhang:
Federated Learning on Non-IID Graphs via Structural Knowledge Sharing. CoRR abs/2211.13009 (2022) - 2021
- [j24]Zhineng Fu, Weijun Xu, Ruiqi Hu, Guodong Long, Jing Jiang:
MHieR-encoder: Modelling the high-frequency changes across stocks. Knowl. Based Syst. 224: 107092 (2021) - [j23]Xiaohan Zhang, Lu Liu, Guodong Long, Jing Jiang, Shenquan Liu:
Episodic memory governs choices: An RNN-based reinforcement learning model for decision-making task. Neural Networks 134: 1-10 (2021) - [c67]Xueping Peng, Guodong Long, Tao Shen, Sen Wang, Jing Jiang:
Sequential Diagnosis Prediction with Transformer and Ontological Representation. ICDM 2021: 489-498 - [c66]Lu Liu, William L. Hamilton, Guodong Long, Jing Jiang, Hugo Larochelle:
A Universal Representation Transformer Layer for Few-Shot Image Classification. ICLR 2021 - [c65]Lu Liu, Tianyi Zhou, Guodong Long, Jing Jiang, Xuanyi Dong, Chengqi Zhang:
Isometric Propagation Network for Generalized Zero-shot Learning. ICLR 2021 - [c62]Shuang Ao, Tianyi Zhou, Guodong Long, Qinghua Lu, Liming Zhu, Jing Jiang:
CO-PILOT: COllaborative Planning and reInforcement Learning On sub-Task curriculum. NeurIPS 2021: 10444-10456 - [i55]Lu Liu, Tianyi Zhou, Guodong Long, Jing Jiang, Xuanyi Dong, Chengqi Zhang:
Isometric Propagation Network for Generalized Zero-shot Learning. CoRR abs/2102.02038 (2021) - [i52]Xiaohan Zhang, Lu Liu, Guodong Long, Jing Jiang, Shenquan Liu:
Episodic memory governs choices: An RNN-based reinforcement learning model for decision-making task. CoRR abs/2103.03679 (2021) - [i51]Yue Tan, Guodong Long, Lu Liu, Tianyi Zhou, Jing Jiang:
FedProto: Federated Prototype Learning over Heterogeneous Devices. CoRR abs/2105.00243 (2021) - [i50]Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, Chengqi Zhang:
Beyond Low-pass Filtering: Graph Convolutional Networks with Automatic Filtering. CoRR abs/2107.04755 (2021) - [i48]Guodong Long, Yue Tan, Jing Jiang, Chengqi Zhang:
Federated Learning for Open Banking. CoRR abs/2108.10749 (2021) - [i47]Guodong Long, Tao Shen, Yue Tan, Leah Gerrard, Allison Clarke, Jing Jiang:
Federated Learning for Privacy-Preserving Open Innovation Future on Digital Health. CoRR abs/2108.10761 (2021) - [i44]Xueping Peng, Guodong Long, Tao Shen, Sen Wang, Jing Jiang:
Sequential Diagnosis Prediction with Transformer and Ontological Representation. CoRR abs/2109.03069 (2021) - [i43]Yang Li, Guodong Long, Tao Shen, Jing Jiang:
Hierarchical Relation-Guided Type-Sentence Alignment for Long-Tail Relation Extraction with Distant Supervision. CoRR abs/2109.09036 (2021) - 2020
- [j17]Shirui Pan, Ruiqi Hu, Sai-Fu Fung, Guodong Long, Jing Jiang, Chengqi Zhang:
Learning Graph Embedding With Adversarial Training Methods. IEEE Trans. Cybern. 50(6): 2475-2487 (2020) - [j16]Jing Jiang, Shaoxiong Ji, Guodong Long:
Decentralized Knowledge Acquisition for Mobile Internet Applications. World Wide Web 23(5): 2653-2669 (2020) - [c60]Ruiqi Hu, Shirui Pan, Guodong Long, Qinghua Lu, Liming Zhu, Jing Jiang:
Going Deep: Graph Convolutional Ladder-Shape Networks. AAAI 2020: 2838-2845 - [c59]Lu Liu, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang:
Attribute Propagation Network for Graph Zero-Shot Learning. AAAI 2020: 4868-4875 - [c58]Yang Li, Guodong Long, Tao Shen, Tianyi Zhou, Lina Yao, Huan Huo, Jing Jiang:
Self-Attention Enhanced Selective Gate with Entity-Aware Embedding for Distantly Supervised Relation Extraction. AAAI 2020: 8269-8276 - [c56]Han Zheng, Jing Jiang, Pengfei Wei, Guodong Long, Chengqi Zhang:
Competitive and Cooperative Heterogeneous Deep Reinforcement Learning. AAMAS 2020: 1656-1664 - [c55]Hao Huang, Guodong Long, Tao Shen, Jing Jiang, Chengqi Zhang:
RatE: Relation-Adaptive Translating Embedding for Knowledge Graph Completion. COLING 2020: 556-567 - [c54]Yang Li, Tao Shen, Guodong Long, Jing Jiang, Tianyi Zhou, Chengqi Zhang:
Improving Long-Tail Relation Extraction with Collaborating Relation-Augmented Attention. COLING 2020: 1653-1664 - [c52]Xueping Peng, Guodong Long, Tao Shen, Sen Wang, Jing Jiang, Chengqi Zhang:
BiteNet: Bidirectional Temporal Encoder Network to Predict Medical Outcomes. ICDM 2020: 412-421 - [c51]Chun Wang, Bo Han, Shirui Pan, Jing Jiang, Gang Niu, Guodong Long:
Cross-Graph: Robust and Unsupervised Embedding for Attributed Graphs with Corrupted Structure. ICDM 2020: 571-580 - [c50]Tao Shen, Xiubo Geng, Guodong Long, Jing Jiang, Chengqi Zhang, Daxin Jiang:
Effective Search of Logical Forms for Weakly Supervised Knowledge-Based Question Answering. IJCAI 2020: 2227-2233 - [c48]Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, Xiaojun Chang, Chengqi Zhang:
Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks. KDD 2020: 753-763 - [c47]Han Zheng, Pengfei Wei, Jing Jiang, Guodong Long, Qinghua Lu, Chengqi Zhang:
Cooperative Heterogeneous Deep Reinforcement Learning. NeurIPS 2020 - [c46]Bingcong Li, Bo Han, Zhuowei Wang, Jing Jiang, Guodong Long:
Confusable Learning for Large-Class Few-Shot Classification. ECML/PKDD (2) 2020: 707-723 - [c45]Xueping Peng, Guodong Long, Tao Shen, Sen Wang, Jing Jiang:
Self-attention Enhanced Patient Journey Understanding in Healthcare System. ECML/PKDD (3) 2020: 719-735 - [p1]Guodong Long, Yue Tan, Jing Jiang, Chengqi Zhang:
Federated Learning for Open Banking. Federated Learning 2020: 240-254 - [i41]Wensi Tang, Guodong Long, Lu Liu, Tianyi Zhou, Jing Jiang, Michael Blumenstein:
Rethinking 1D-CNN for Time Series Classification: A Stronger Baseline. CoRR abs/2002.10061 (2020) - [i38]Ming Xie, Guodong Long, Tao Shen, Tianyi Zhou, Xianzhi Wang, Jing Jiang:
Multi-Center Federated Learning. CoRR abs/2005.01026 (2020) - [i37]Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, Xiaojun Chang, Chengqi Zhang:
Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks. CoRR abs/2005.11650 (2020) - [i35]Xueping Peng, Guodong Long, Tao Shen, Sen Wang, Jing Jiang:
Self-Attention Enhanced Patient Journey Understanding in Healthcare System. CoRR abs/2006.10516 (2020) - [i34]Lu Liu, William L. Hamilton, Guodong Long, Jing Jiang, Hugo Larochelle:
A Universal Representation Transformer Layer for Few-Shot Image Classification. CoRR abs/2006.11702 (2020) - [i33]Lu Liu, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang:
Many-Class Few-Shot Learning on Multi-Granularity Class Hierarchy. CoRR abs/2006.15479 (2020) - [i32]Lu Liu, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang:
Attribute Propagation Network for Graph Zero-shot Learning. CoRR abs/2009.11816 (2020) - [i31]Xueping Peng, Guodong Long, Tao Shen, Sen Wang, Jing Jiang, Chengqi Zhang:
BiteNet: Bidirectional Temporal Encoder Network to Predict Medical Outcomes. CoRR abs/2009.13252 (2020) - [i30]Yang Li, Tao Shen, Guodong Long, Jing Jiang, Tianyi Zhou, Chengqi Zhang:
Improving Long-Tail Relation Extraction with Collaborating Relation-Augmented Attention. CoRR abs/2010.03773 (2020) - [i29]Hao Huang, Guodong Long, Tao Shen, Jing Jiang, Chengqi Zhang:
RatE: Relation-Adaptive Translating Embedding for Knowledge Graph Completion. CoRR abs/2010.04863 (2020) - [i28]Han Zheng, Pengfei Wei, Jing Jiang, Guodong Long, Qinghua Lu, Chengqi Zhang:
Cooperative Heterogeneous Deep Reinforcement Learning. CoRR abs/2011.00791 (2020) - [i27]Bingcong Li, Bo Han, Zhuowei Wang, Jing Jiang, Guodong Long:
Confusable Learning for Large-class Few-Shot Classification. CoRR abs/2011.03154 (2020) - [i26]Zhuowei Wang, Jing Jiang, Bo Han, Lei Feng, Bo An, Gang Niu, Guodong Long:
SemiNLL: A Framework of Noisy-Label Learning by Semi-Supervised Learning. CoRR abs/2012.00925 (2020) - 2019
- [j13]Xinxin Jiang, Shirui Pan, Guodong Long, Fei Xiong, Jing Jiang, Chengqi Zhang:
Cost-Sensitive Parallel Learning Framework for Insurance Intelligence Operation. IEEE Trans. Ind. Electron. 66(12): 9713-9723 (2019) - [c42]Shaoxiong Ji, Guodong Long, Shirui Pan, Tianqing Zhu, Jing Jiang, Sen Wang:
Detecting Suicidal Ideation with Data Protection in Online Communities. DASFAA (3) 2019: 225-229 - [c40]Xueping Peng, Guodong Long, Tao Shen, Sen Wang, Jing Jiang, Michael Blumenstein:
Temporal Self-Attention Network for Medical Concept Embedding. ICDM 2019: 498-507 - [c38]Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, Chengqi Zhang:
Graph WaveNet for Deep Spatial-Temporal Graph Modeling. IJCAI 2019: 1907-1913 - [c37]Lu Liu, Tianyi Zhou, Guodong Long, Jing Jiang, Lina Yao, Chengqi Zhang:
Prototype Propagation Networks (PPN) for Weakly-supervised Few-shot Learning on Category Graph. IJCAI 2019: 3015-3022 - [c36]Chun Wang, Shirui Pan, Ruiqi Hu, Guodong Long, Jing Jiang, Chengqi Zhang:
Attributed Graph Clustering: A Deep Attentional Embedding Approach. IJCAI 2019: 3670-3676 - [c35]Fengwen Chen, Shirui Pan, Jing Jiang, Huan Huo, Guodong Long:
DAGCN: Dual Attention Graph Convolutional Networks. IJCNN 2019: 1-8 - [c34]Shaoxiong Ji, Shirui Pan, Guodong Long, Xue Li, Jing Jiang, Zi Huang:
Learning Private Neural Language Modeling with Attentive Aggregation. IJCNN 2019: 1-8 - [c33]Xueping Peng, Guodong Long, Shirui Pan, Jing Jiang, Zhendong Niu:
Attentive Dual Embedding for Understanding Medical Concepts in Electronic Health Records. IJCNN 2019: 1-8 - [c31]Tao Shen, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang:
Tensorized Self-Attention: Efficiently Modeling Pairwise and Global Dependencies Together. NAACL-HLT (1) 2019: 1256-1266 - [c30]Lu Liu, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang:
Learning to Propagate for Graph Meta-Learning. NeurIPS 2019: 1037-1048 - [i24]Shirui Pan, Ruiqi Hu, Sai-Fu Fung, Guodong Long, Jing Jiang, Chengqi Zhang:
Learning Graph Embedding with Adversarial Training Methods. CoRR abs/1901.01250 (2019) - [i23]Fengwen Chen, Shirui Pan, Jing Jiang, Huan Huo, Guodong Long:
DAGCN: Dual Attention Graph Convolutional Networks. CoRR abs/1904.02278 (2019) - [i22]Lu Liu, Tianyi Zhou, Guodong Long, Jing Jiang, Lina Yao, Chengqi Zhang:
Prototype Propagation Networks (PPN) for Weakly-supervised Few-shot Learning on Category Graph. CoRR abs/1905.04042 (2019) - [i21]Shaoxiong Ji, Guodong Long, Shirui Pan, Tianqing Zhu, Jing Jiang, Sen Wang, Xue Li:
Decentralized Learning with Average Difference Aggregation for Proactive Online Social Care. CoRR abs/1905.07665 (2019) - [i20]Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, Chengqi Zhang:
Graph WaveNet for Deep Spatial-Temporal Graph Modeling. CoRR abs/1906.00121 (2019) - [i19]Chun Wang, Shirui Pan, Ruiqi Hu, Guodong Long, Jing Jiang, Chengqi Zhang:
Attributed Graph Clustering: A Deep Attentional Embedding Approach. CoRR abs/1906.06532 (2019) - [i18]Tao Shen, Xiubo Geng, Tao Qin, Guodong Long, Jing Jiang, Daxin Jiang:
Effective Search of Logical Forms for Weakly Supervised Knowledge-Based Question Answering. CoRR abs/1909.02762 (2019) - [i17]Lu Liu, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang:
Learning to Propagate for Graph Meta-Learning. CoRR abs/1909.05024 (2019) - [i16]Xueping Peng, Guodong Long, Tao Shen, Sen Wang, Jing Jiang, Michael Blumenstein:
Temporal Self-Attention Network for Medical Concept Embedding. CoRR abs/1909.06886 (2019) - [i13]Yang Li, Guodong Long, Tao Shen, Tianyi Zhou, Lina Yao, Huan Huo, Jing Jiang:
Self-Attention Enhanced Selective Gate with Entity-Aware Embedding for Distantly Supervised Relation Extraction. CoRR abs/1911.11899 (2019) - 2018
- [c28]Tao Shen, Tianyi Zhou, Guodong Long, Jing Jiang, Shirui Pan, Chengqi Zhang:
DiSAN: Directional Self-Attention Network for RNN/CNN-Free Language Understanding. AAAI 2018: 5446-5455 - [c26]Tao Shen, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang:
Bi-Directional Block Self-Attention for Fast and Memory-Efficient Sequence Modeling. ICLR (Poster) 2018 - [c25]Shirui Pan, Ruiqi Hu, Guodong Long, Jing Jiang, Lina Yao, Chengqi Zhang:
Adversarially Regularized Graph Autoencoder for Graph Embedding. IJCAI 2018: 2609-2615 - [c22]Tao Shen, Tianyi Zhou, Guodong Long, Jing Jiang, Sen Wang, Chengqi Zhang:
Reinforced Self-Attention Network: a Hybrid of Hard and Soft Attention for Sequence Modeling. IJCAI 2018: 4345-4352 - [c21]Xinxin Jiang, Shirui Pan, Jing Jiang, Guodong Long:
Cross-Domain Deep Learning Approach For Multiple Financial Market Prediction. IJCNN 2018: 1-8 - [c20]Xinxin Jiang, Shirui Pan, Guodong Long, Jiang Chang, Jing Jiang, Chengqi Zhang:
Cost-sensitive Hybrid Neural Networks for Heterogeneous and Imbalanced Data. IJCNN 2018: 1-8 - [i12]Tao Shen, Tianyi Zhou, Guodong Long, Jing Jiang, Sen Wang, Chengqi Zhang:
Reinforced Self-Attention Network: a Hybrid of Hard and Soft Attention for Sequence Modeling. CoRR abs/1801.10296 (2018) - [i11]Shirui Pan, Ruiqi Hu, Guodong Long, Jing Jiang, Lina Yao, Chengqi Zhang:
Adversarially Regularized Graph Autoencoder. CoRR abs/1802.04407 (2018) - [i10]Tao Shen, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang:
Bi-Directional Block Self-Attention for Fast and Memory-Efficient Sequence Modeling. CoRR abs/1804.00857 (2018) - [i8]Tao Shen, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang:
Fast Directional Self-Attention Mechanism. CoRR abs/1805.00912 (2018) - [i4]Shaoxiong Ji, Shirui Pan, Guodong Long, Xue Li, Jing Jiang, Zi Huang:
Learning Private Neural Language Modeling with Attentive Aggregation. CoRR abs/1812.07108 (2018) - 2017
- [c19]Chun Wang, Shirui Pan, Guodong Long, Xingquan Zhu, Jing Jiang:
MGAE: Marginalized Graph Autoencoder for Graph Clustering. CIKM 2017: 889-898 - [c18]Ruiqi Hu, Shirui Pan, Jing Jiang, Guodong Long:
Graph Ladder Networks for Network Classification. CIKM 2017: 2103-2106 - [i3]Tao Shen, Tianyi Zhou, Guodong Long, Jing Jiang, Shirui Pan, Chengqi Zhang:
DiSAN: Directional Self-Attention Network for RNN/CNN-free Language Understanding. CoRR abs/1709.04696 (2017) - 2016
- [c12]Ruiqi Hu, Shirui Pan, Guodong Long, Xingquan Zhu, Jing Jiang, Chengqi Zhang:
Co-clustering enterprise social networks. IJCNN 2016: 107-114 - [c11]Yu Bai, Haishuai Wang, Jia Wu, Yun Zhang, Jing Jiang, Guodong Long:
Evolutionary lazy learning for Naive Bayes classification. IJCNN 2016: 3124-3129 - 2013
- [c4]Guodong Long, Jing Jiang:
Graph Based Feature Augmentation for Short and Sparse Text Classification. ADMA (1) 2013: 456-467 - [c3]Jing Jiang, Jie Lu, Guangquan Zhang, Guodong Long:
Optimal Cloud Resource Auto-Scaling for Web Applications. CCGRID 2013: 58-65 - 2011
- [c1]Jing Jiang, Jie Lu, Guangquan Zhang, Guodong Long:
Scaling-Up Item-Based Collaborative Filtering Recommendation Algorithm Based on Hadoop. SERVICES 2011: 490-497
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