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Qimai LI
Qimai LI
Parametrix.AI
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Titel
Zitiert von
Zitiert von
Jahr
Deeper insights into graph convolutional networks for semi-supervised learning
Q Li, Z Han, XM Wu
Proceedings of the AAAI conference on artificial intelligence 32 (1), 2018
28242018
Attributed graph clustering via adaptive graph convolution
X Zhang, H Liu, Q Li, XM Wu
arXiv preprint arXiv:1906.01210, 2019
2902019
Label efficient semi-supervised learning via graph filtering
Q Li, XM Wu, H Liu, X Zhang, Z Guan
Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2019
1932019
Unknown intent detection using Gaussian mixture model with an application to zero-shot intent classification
G Yan, L Fan, Q Li, H Liu, X Zhang, XM Wu, AYS Lam
Proceedings of the 58th annual meeting of the association for computational …, 2020
772020
Large margin meta-learning for few-shot classification
Y Wang, XM Wu, Q Li, J Gu, W Xiang, L Zhang, VOK Li
Workshop on Meta-Learning (MetaLearn 2018)@ NeurIPS 2018, 2018
49*2018
Reconstructing capsule networks for zero-shot intent classification
H Liu, X Zhang, L Fan, X Fu, Q Li, XM Wu, AYS Lam
Proceedings of the 2019 Conference on Empirical Methods in Natural Language …, 2019
432019
A closer look at the training strategy for modern meta-learning
J Chen, XM Wu, Y Li, Q Li, LM Zhan, F Chung
Advances in Neural Information Processing Systems 33, 396-406, 2020
352020
Personalized knowledge-aware recommendation with collaborative and attentive graph convolutional networks
Q Dai, XM Wu, L Fan, Q Li, H Liu, X Zhang, D Wang, G Lin, K Yang
Pattern Recognition 128, 108628, 2022
252022
Recon: Reducing conflicting gradients from the root for multi-task learning
G Shi, Q Li, W Zhang, J Chen, XM Wu
arXiv preprint arXiv:2302.11289, 2023
202023
Dimensionwise separable 2-D graph convolution for unsupervised and semi-supervised learning on graphs
Q Li, X Zhang, H Liu, Q Dai, XM Wu
Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data …, 2021
14*2021
Modeling user behavior with graph convolution for personalized product search
L Fan, Q Li, B Liu, XM Wu, X Zhang, F Lv, G Lin, S Li, T Jin, K Yang
Proceedings of the ACM Web Conference 2022, 203-212, 2022
132022
Using human feedback to fine-tune diffusion models without any reward model
K Yang, J Tao, J Lyu, C Ge, J Chen, W Shen, X Zhu, X Li
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2024
92024
RPC: Representative possible world based consistent clustering algorithm for uncertain data
H Liu, X Zhang, X Zhang, Q Li, XM Wu
Computer Communications 176, 128-137, 2021
8*2021
Multi-agent path finding via tree lstm
Y Jiang, K Zhang, Q Li, J Chen, X Zhu
arXiv preprint arXiv:2210.12933, 2022
32022
Neural MMO 2.0: A Massively Multi-task Addition to Massively Multi-agent Learning
J Suarez, D Bloomin, KW Choe, HX Li, R Sullivan, N Kanna, D Scott, ...
Advances in Neural Information Processing Systems 36, 2024
2024
Boosting decision-based black-box adversarial attack with gradient priors
H Liu, X Huang, X Zhang, Q Li, F Ma, W Wang, H Chen, H Yu, X Zhang
arXiv preprint arXiv:2310.19038, 2023
2023
Adaptive Graph Convolution Methods for Attributed Graph Clustering
X Zhang, H Liu, Q Li, XM Wu, X Zhang
IEEE Transactions on Knowledge and Data Engineering, 2023
2023
Simple yet Effective Gradient-Free Graph Convolutional Networks
Y Zhu, X Ai, Q Li, XM Wu, K Zhou
arXiv preprint arXiv:2302.00371, 2023
2023
Learning on graphs with graph convolution
Q Li
Hong Kong Polytechnic University, 2023
2023
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