Jin-Hui Wu — accepted AI-conference papers

2026

On the Privacy-Preserving Capabilities of PAVE Specifications in Learnware

Hao-Yi Lei, Jin-Hui Wu, Zhi-Hao Tan, Zhi-Hua Zhou

IJCAI 2026

The learnware paradigm supports model reuse by pairing each submitted model with a specification, a lightweight representation used by the learnware dock system to identify, match, and reuse models without accessing raw data. While specifications are essential for learnware identification, they are

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2023

Complex-valued Neurons Can Learn More but Slower than Real-valued Neurons via Gradient Descent

Jin-Hui Wu, Shao-Qun Zhang, Yuan Jiang, Zhi-Hua Zhou

NeurIPS 2023poster

Complex-valued neural networks potentially possess better representations and performance than real-valued counterparts when dealing with some complicated tasks such as acoustic analysis, radar image classification, etc. Despite empirical successes, it remains unknown theoretically when and to what…

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