Nan Yin
Nan Yin
Mohamed bin Zayed University of Artificial Intelligence
Verified email at
Cited by
Cited by
Coco: A coupled contrastive framework for unsupervised domain adaptive graph classification
N Yin, L Shen, M Wang, L Lan, Z Ma, C Chen, XS Hua, X Luo
International Conference on Machine Learning, 40040-40053, 2023
Dynamic hypergraph convolutional network
N Yin, F Feng, Z Luo, X Zhang, W Wang, X Luo, C Chen, XS Hua
2022 IEEE 38th International Conference on Data Engineering (ICDE), 1621-1634, 2022
Deal: An unsupervised domain adaptive framework for graph-level classification
N Yin, L Shen, B Li, M Wang, X Luo, C Chen, Z Luo, XS Hua
Proceedings of the 30th ACM International Conference on Multimedia, 3470-3479, 2022
Omg: towards effective graph classification against label noise
N Yin, L Shen, M Wang, X Luo, Z Luo, D Tao
IEEE Transactions on Knowledge and Data Engineering, 2023
Messages are never propagated alone: Collaborative hypergraph neural network for time-series forecasting
N Yin, L Shen, H Xiong, B Gu, C Chen, XS Hua, S Liu, X Luo
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023
Deep imbalanced learning for multimodal emotion recognition in conversations
T Meng, Y Shou, W Ai, N Yin, K Li
arXiv preprint arXiv:2312.06337, 2023
A comprehensive survey on multi-modal conversational emotion recognition with deep learning
Y Shou, T Meng, W Ai, N Yin, K Li
arXiv preprint arXiv:2312.05735, 2023
SA-GDA: Spectral Augmentation for Graph Domain Adaptation
J Pang, Z Wang, J Tang, M Xiao, N Yin
ACMMM, 2023
A focally discriminative loss for unsupervised domain adaptation
D Sun, M Wang, X Ma, T Zhang, N Yin, W Yu, Z Luo
Neural Information Processing: 28th International Conference, ICONIP 2021 …, 2021
Generic structure extraction with bi-level optimization for graph structure learning
N Yin, Z Luo
Entropy 24 (9), 1228, 2022
Dynamic spiking graph neural networks
N Yin, M Wang, Z Chen, G De Masi, H Xiong, B Gu
Proceedings of the AAAI Conference on Artificial Intelligence 38 (15), 16495 …, 2024
Asymmetrically decentralized federated learning
Q Li, M Zhang, N Yin, Q Yin, L Shen
arXiv preprint arXiv:2310.05093, 2023
A Survey of Graph Neural Networks in Real world: Imbalance, Noise, Privacy and OOD Challenges
W Ju, S Yi, Y Wang, Z Xiao, Z Mao, H Li, Y Gu, Y Qin, N Yin, S Wang, ...
arXiv preprint arXiv:2403.04468, 2024
DREAM: Dual Structured Exploration with Mixup for Open-set Graph Domain Adaption
N Yin, M Wang, Z Chen, L Shen, H Xiong, B Gu, X Luo
The Twelfth International Conference on Learning Representations, 2023
Entity-aware biaffine attention for constituent parsing
X Bai, N Yin, X Zhang, X Wang, Z Luo
Artificial Neural Networks and Machine Learning–ICANN 2021: 30th …, 2021
Revisiting Multi-modal Emotion Learning with Broad State Space Models and Probability-guidance Fusion
Y Shou, T Meng, F Zhang, N Yin, K Li
arXiv preprint arXiv:2404.17858, 2024
Revisiting Multimodal Emotion Recognition in Conversation from the Perspective of Graph Spectrum
T Meng, F Zhang, Y Shou, W Ai, N Yin, K Li
arXiv preprint arXiv:2404.17862, 2024
Continuous Spiking Graph Neural Networks
N Yin, M Wan, L Shen, HL Patel, B Li, B Gu, H Xiong
arXiv preprint arXiv:2404.01897, 2024
Merging Multi-Task Models via Weight-Ensembling Mixture of Experts
A Tang, L Shen, Y Luo, N Yin, L Zhang, D Tao
arXiv preprint arXiv:2402.00433, 2024
Continual Learning From a Stream of APIs
E Yang, Z Wang, L Shen, N Yin, T Liu, G Guo, X Wang, D Tao
arXiv preprint arXiv:2309.00023, 2023
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