Zhilin Huang

Orcid: 0000-0003-3417-743X

According to our database1, Zhilin Huang authored at least 14 papers between 2020 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Links

On csauthors.net:

Bibliography

2024
Individual and Structural Graph Information Bottlenecks for Out-of-Distribution Generalization.
IEEE Trans. Knowl. Data Eng., February, 2024

Binding-Adaptive Diffusion Models for Structure-Based Drug Design.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023
Multiple Objective Fairness Scheduling Optimization Algorithms Based on Multiple DAGs in Heterogeneous Edge Computing.
J. Circuits Syst. Comput., May, 2023

Improving Diffusion-Based Image Synthesis with Context Prediction.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022
Diffusion-Based Scene Graph to Image Generation with Masked Contrastive Pre-Training.
CoRR, 2022

Region-aware Attention for Image Inpainting.
CoRR, 2022

Effects of Imitation Training Methods on Chinese EFL Learners' Production of English Intonation.
Proceedings of the International Conference on Asian Language Processing, 2022

2021
Structure-aware Image Inpainting with Two Parallel Streams.
CoRR, 2021

Gated Character-aware Convolutional Neural Network for Effective Automated Essay Scoring.
Proceedings of the WI-IAT '21: IEEE/WIC/ACM International Conference on Web Intelligence, Melbourne VIC Australia, December 14, 2021

Confidence-Based Global Attention Guided Network for Image Inpainting.
Proceedings of the MultiMedia Modeling - 27th International Conference, 2021

Bi-encoder Network with Structure-texture Consistency for Image Inpainting.
Proceedings of the International Joint Conference on Neural Networks, 2021

Semantic-Aware Context Aggregation for Image Inpainting.
Proceedings of the IEEE International Conference on Acoustics, 2021

2020
Mining incomplete clinical data for the early assessment of Kawasaki disease based on feature clustering and convolutional neural networks.
Artif. Intell. Medicine, 2020

Integrating Co-Clustering and Interpretable Machine Learning for the Prediction of Intravenous Immunoglobulin Resistance in Kawasaki Disease.
IEEE Access, 2020


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