Articles with public access mandates - Hao WangLearn more
Available somewhere: 24
Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model
X Shi, H Wang, Z Gao, L Lausen, DY Yeung, W WOO, W Wong
Advances in Neural Information Processing Systems, 5620-5630, 2017
Mandates: Research Grants Council, Hong Kong
Rethinking knowledge graph propagation for zero-shot learning
M Kampffmeyer, Y Chen, X Liang, H Wang, Y Zhang, EP Xing
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019
Mandates: Research Council of Norway
Artificial intelligence-enabled detection and assessment of Parkinson’s disease using nocturnal breathing signals
Y Yang, Y Yuan, G Zhang, H Wang, YC Chen, Y Liu, CG Tarolli, ...
Nature Medicine 28 (10), 2207-2215, 2022
Mandates: US National Science Foundation, US National Institutes of Health, Michael J …
Relational Stacked Denoising Autoencoder for Tag Recommendation
H Wang, X Shi, DY Yeung
AAAI Conference on Artificial Intelligence (AAAI), 3052-3058, 2015
Mandates: Research Grants Council, Hong Kong
Deep Learning and the Weather Forecasting Problem: Precipitation Nowcasting
Z Gao, X Shi, H Wang, DY Yeung, W Woo, WK Wong
Book Chapter of Deep Learning for the Earth Sciences: A Comprehensive …, 2021
Mandates: Research Grants Council, Hong Kong
Relational Deep Learning: A Deep Latent Variable Model for Link Prediction
H Wang, X Shi, DY Yeung
AAAI Conference on Artificial Intelligence (AAAI), 2688-2694, 2017
Mandates: Research Grants Council, Hong Kong
Relational collaborative topic regression for recommender systems
H Wang, WJ Li
IEEE Transactions on Knowledge and Data Engineering 27 (5), 1343-1355, 2015
Mandates: National Natural Science Foundation of China
OrphicX: A causality-inspired latent variable model for interpreting graph neural networks
W Lin, H Lan, H Wang, B Li
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022
Mandates: US National Science Foundation
Generative Interventions for Causal Learning
C Mao, A Gupta, A Cha, H Wang, J Yang, C Vondrick
CVPR 2021, 2020
Mandates: US National Science Foundation, US Department of Defense
Adversarial Attacks are Reversible with Natural Supervision
C Mao, M Chiquier, H Wang, J Yang, C Vondrick
ICCV 2021, 2021
Mandates: US National Science Foundation, US Department of Defense
Training-free uncertainty estimation for neural networks
L Mi, H Wang, Y Tian, N Shavit
AAAI Conference on Artificial Intelligence (AAAI), 2022
Mandates: US National Science Foundation
Causal Transportability for Visual Recognition
C Mao, K Xia, J Wang, H Wang, J Yang, E Bareinboim, C Vondrick
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022
Mandates: US National Science Foundation, US Department of Defense
Recurrent Poisson Process Unit for Speech Recognition
H Huang, H Wang, B Mak
AAAI Conference on Artificial Intelligence (AAAI), 2019
Mandates: Research Grants Council, Hong Kong
Social ODE: Multi-agent Trajectory Forecasting with Neural Ordinary Differential Equations
S Wen, H Wang, D Metaxas
European Conference on Computer Vision (ECCV), 217-233, 2022
Mandates: US National Science Foundation, US Department of Defense
Authenticating on-body IoT devices: An adversarial learning approach
Y Huang, W Wang, H Wang, T Jiang, Q Zhang
IEEE Transactions on Wireless Communications 19 (8), 5234-5245, 2020
Mandates: National Natural Science Foundation of China
Counterfactual Collaborative Reasoning
J Ji, Z Li, S Xu, M Xiong, J Tan, Y Ge, H Wang, Y Zhang
Proceedings of the Sixteenth ACM International Conference on Web Search and …, 2023
Mandates: US National Science Foundation
A Unified Approach to Domain Incremental Learning with Memory: Theory and Algorithm
H Shi, H Wang
Advances in Neural Information Processing Systems (NeurIPS), 2023
Mandates: US National Science Foundation
Energy-based concept bottleneck models: Unifying prediction, concept intervention, and probabilistic interpretations
X Xu, Y Qin, L Mi, H Wang, X Li
International Conference on Learning Representations (ICLR), 2024
Mandates: Research Grants Council, Hong Kong
Taxonomy-Structured Domain Adaptation
T Liu, Z Xu, H He, GY Hao, GH Lee, H Wang
International Conference on Machine Learning (ICML), 2023
Mandates: US National Science Foundation
Self-Interpretable Time Series Prediction with Counterfactual Explanations
J Yan, H Wang
International Conference on Machine Learning (ICML), 2023
Mandates: US National Science Foundation
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