Cheems Wang(王琦)
Cheems Wang(王琦)
Tsinghua University, Universiteit van Amsterdam
Verified email at - Homepage
Cited by
Cited by
A Novel Ensemble Method for Imbalanced Data Learning: Bagging of Extrapolation‐SMOTE SVM
Q Wang, ZH Luo, JC Huang, YH Feng, Z Liu
Computational intelligence and neuroscience 2017 (1), 1827016, 2017
Drug target protein‐protein interaction networks: a systematic perspective
Y Feng, Q Wang, T Wang
BioMed research international 2017 (1), 1289259, 2017
CSAN: A neural network benchmark model for crime forecasting in spatio-temporal scale
Q Wang, G Jin, X Zhao, Y Feng, J Huang
Knowledge-Based Systems 189, 105120, 2020
A novel framework for the identification of drug target proteins: Combining stacked auto-encoders with a biased support vector machine
Q Wang, YH Feng, JC Huang, TJ Wang, GQ Cheng
PloS one 12 (4), e0176486, 2017
Doubly stochastic variational inference for neural processes with hierarchical latent variables
Q Wang, H Van Hoof
ICML 2020, 2020
Urban Fire Situation Forecasting: Deep sequence learning with spatio-temporal dynamics
G Jin, Q Wang, C Zhu, Y Feng, J Huang, X Hu
Applied Soft Computing 97, 106730, 2020
Model-based meta reinforcement learning using graph structured surrogate models and amortized policy search
Q Wang, H Van Hoof
ICML 2022 Spotlight, 2022
Crime-GAN: A context-based sequence generative network for crime forecasting with adversarial loss
G Jin, Q Wang, X Zhao, Y Feng, Q Cheng, J Huang
2019 IEEE International Conference on Big Data (Big Data), 1460-1469, 2019
On the improvement of reinforcement active learning with the involvement of cross entropy to address one-shot learning problem
H Huang, J Huang, Y Feng, J Zhang, Z Liu, Q Wang, L Chen
PloS one 14 (6), e0217408, 2019
多 Agent 深度强化学习综述
梁星星, 冯旸赫, 马扬, 程光权, 黄金才, 王琦, 周玉珍, 刘忠
自动化学报 46 (12), 2537-2557, 2020
Learning expressive meta-representations with mixture of expert neural processes
Q Wang, H van Hoof
NeurIPS 2022, 2022
Novel deep reinforcement learning algorithm based on attention-based value function and autoregressive environment model
梁星星, 冯旸赫, 黄金才, 王琦, 马扬, 刘忠
Journal of Software 31 (4), 948-966, 2020
Bridge the Inference Gaps of Neural Processes via Expectation Maximization
Q Wang, M Federici, H van Hoof
ICLR 2023, 2023
Episodic Multi-Task Learning with Heterogeneous Neural Processes
J Shen, X Zhen, C Wang, M Worring
NeurIPS 2023 Spotlight, 2023
Large-scale generative simulation artificial intelligence: The next hotspot
Q Wang, Y Feng, J Huang, Y Lv, Z Xie, X Gao
The Innovation 4 (6), 2023
A Simple Yet Effective Strategy to Robustify the Meta Learning Paradigm
Q Wang, Y Lv, Y Feng, Z Xie, J Huang
NeurIPS 2023, 2023
GO4Align: Group Optimization for Multi-Task Alignment
J Shen, C Wang, Z Xiao, N Van Noord, M Worring
arXiv preprint arXiv:2404.06486, 2024
Non-informative noise-enhanced stochastic neural networks for improving adversarial robustness
H Yang, M Wang, Q Wang, Z Yu, G Jin, C Zhou, Y Zhou
Information Fusion 108, 102397, 2024
Balanced Confidence Calibration for Graph Neural Networks
H Yang, M Wang, Q Wang, M Lao, Y Zhou
KDD 2024, 2024
Reducing Fine-Tuning Memory Overhead by Approximate and Memory-Sharing Backpropagation
Y Yang, Y Shi, C Wang, X Zhen, Y Shi, J Xu
ICML 2024, 2024
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