1 code implementation • 24 Apr 2024 • Henry Peng Zou, Vinay Samuel, Yue Zhou, Weizhi Zhang, Liancheng Fang, Zihe Song, Philip S. Yu, Cornelia Caragea
To address these limitations, we present ImplicitAVE, the first, publicly available multimodal dataset for implicit attribute value extraction.
no code implementations • 24 Apr 2024 • Weizhi Zhang, Liangwei Yang, Zihe Song, Henry Peng Zou, Ke Xu, Yuanjie Zhu, Philip S. Yu
Graph contrastive learning aims to learn from high-order collaborative filtering signals with unsupervised augmentation on the user-item bipartite graph, which predominantly relies on the multi-task learning framework involving both the pair-wise recommendation loss and the contrastive loss.
no code implementations • 11 Jan 2024 • Liangwei Yang, Hengrui Zhang, Zihe Song, Jiawei Zhang, Weizhi Zhang, Jing Ma, Philip S. Yu
This paper answers a fundamental question in artificial neural network (ANN) design: We do not need to build ANNs layer-by-layer sequentially to guarantee the Directed Acyclic Graph (DAG) property.
no code implementations • 21 Nov 2023 • Ke Xu, Yuanjie Zhu, Weizhi Zhang, Philip S. Yu
This inspired us to address the computational limitations of GCN-based models by designing a simple and efficient NODE-based model that can skip some GCN layers to reach the final state, thus avoiding the need to create many layers.
1 code implementation • 23 Oct 2023 • Henry Peng Zou, Yue Zhou, Weizhi Zhang, Cornelia Caragea
During crisis events, people often use social media platforms such as Twitter to disseminate information about the situation, warnings, advice, and support.