Renjie Lin — accepted AI-conference papers

2026

DIN: Dual Impulse Network for Multi-view Representation Learning

Yilin Wu, Weihong Lin, Renjie Lin, Zihan Fang, Shide Du, Shiping Wang

AAAI 2026technical

Multi-view representation learning, which utilizes multiple channels to improve perceptual accuracy, is recognized for its effectiveness in the analysis of multi-view data. However, deploying these methods in real-world scenarios presents two primary challenges. 1) Lack of Variegation: Multi-view re

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2026

Graph Meets Deep Unfolding: An Interpretable Mutual-benefit Multi-view Learning Network

Renjie Lin, Hongzhi He, Yilin Wu, Shide Du, Le Zhang

AAAI 2026technical

Significant efforts have been focused on enhancing the utilization of multiple node features and topological structures in multi-view graph learning through explicit model-driven and implicit deep learning-based methodologies. The former excels in embedding prior knowledge, thereby offering theoreti

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