Zobrazeno 1 - 10
of 38
pro vyhledávání: '"Meng, Changhua"'
Autor:
Liu, Yunfei, Li, Jintang, Chen, Yuehe, Wu, Ruofan, Wang, Ericbk, Zhou, Jing, Tian, Sheng, Shen, Shuheng, Fu, Xing, Meng, Changhua, Wang, Weiqiang, Chen, Liang
Graph clustering, a fundamental and challenging task in graph mining, aims to classify nodes in a graph into several disjoint clusters. In recent years, graph contrastive learning (GCL) has emerged as a dominant line of research in graph clustering a
Externí odkaz:
http://arxiv.org/abs/2406.14288
Autor:
Wang, Ke, Xia, Tianyu, Gu, Zhangxuan, Zhao, Yi, Shen, Shuheng, Meng, Changhua, Wang, Weiqiang, Xu, Ke
Online GUI navigation on mobile devices has driven a lot of attention recent years since it contributes to many real-world applications. With the rapid development of large language models (LLM), multimodal large language models (MLLM) have tremendou
Externí odkaz:
http://arxiv.org/abs/2406.14250
Large Language Models (LLMs) become the start-of-the-art solutions for a variety of natural language tasks and are integrated into real-world applications. However, LLMs can be potentially harmful in manifesting undesirable safety issues like social
Externí odkaz:
http://arxiv.org/abs/2403.00829
Autor:
Li, Jintang, Wei, Zheng, Dan, Jiawang, Zhou, Jing, Zhu, Yuchang, Wu, Ruofan, Wang, Baokun, Zhen, Zhang, Meng, Changhua, Jin, Hong, Zheng, Zibin, Chen, Liang
Real-world graphs are typically complex, exhibiting heterogeneity in the global structure, as well as strong heterophily within local neighborhoods. While a growing body of literature has revealed the limitations of common graph neural networks (GNNs
Externí odkaz:
http://arxiv.org/abs/2310.11664
Autor:
Dan, Jiawang, Wu, Ruofan, Liu, Yunpeng, Wang, Baokun, Meng, Changhua, Liu, Tengfei, Zhang, Tianyi, Wang, Ningtao, Fu, Xing, Li, Qi, Wang, Weiqiang
Graph representation learning has now become the de facto standard when handling graph-structured data, with the framework of message-passing graph neural networks (MPNN) being the most prevailing algorithmic tool. Despite its popularity, the family
Externí odkaz:
http://arxiv.org/abs/2310.11281
Deep neural networks are vulnerable to adversarial examples, dictating the imperativeness to test the model's robustness before deployment. Transfer-based attackers craft adversarial examples against surrogate models and transfer them to victim model
Externí odkaz:
http://arxiv.org/abs/2308.07625
Latent diffusion models achieve state-of-the-art performance on a variety of generative tasks, such as image synthesis and image editing. However, the robustness of latent diffusion models is not well studied. Previous works only focus on the adversa
Externí odkaz:
http://arxiv.org/abs/2306.08257
Autor:
Li, Jintang, Zhang, Huizhe, Wu, Ruofan, Zhu, Zulun, Wang, Baokun, Meng, Changhua, Zheng, Zibin, Chen, Liang
While contrastive self-supervised learning has become the de-facto learning paradigm for graph neural networks, the pursuit of higher task accuracy requires a larger hidden dimensionality to learn informative and discriminative full-precision represe
Externí odkaz:
http://arxiv.org/abs/2305.19306
Autor:
Tian, Sheng, Dong, Jihai, Li, Jintang, Zhao, Wenlong, Xu, Xiaolong, wang, Baokun, Song, Bowen, Meng, Changhua, Zhang, Tianyi, Chen, Liang
Anomaly detection aims to distinguish abnormal instances that deviate significantly from the majority of benign ones. As instances that appear in the real world are naturally connected and can be represented with graphs, graph neural networks become
Externí odkaz:
http://arxiv.org/abs/2305.13573
Autor:
Chen, Haoxing, Xu, Zhuoer, Gu, Zhangxuan, Lan, Jun, Zheng, Xing, Li, Yaohui, Meng, Changhua, Zhu, Huijia, Wang, Weiqiang
Diffusion model based language-guided image editing has achieved great success recently. However, existing state-of-the-art diffusion models struggle with rendering correct text and text style during generation. To tackle this problem, we propose a u
Externí odkaz:
http://arxiv.org/abs/2305.10825