Zobrazeno 1 - 10
of 25
pro vyhledávání: '"Du, Boxin"'
Autor:
Zhong, Wenliang, Wu, Wenyi, Li, Qi, Barton, Rob, Du, Boxin, Sam, Shioulin, Bouyarmane, Karim, Tutar, Ismail, Huang, Junzhou
Multimodal Large Language Models (MLLMs) have achieved SOTA performance in various visual language tasks by fusing the visual representations with LLMs leveraging some visual adapters. In this paper, we first establish that adapters using query-based
Externí odkaz:
http://arxiv.org/abs/2406.02987
Conversational question answering (convQA) over knowledge graphs (KGs) involves answering multi-turn natural language questions about information contained in a KG. State-of-the-art methods of ConvQA often struggle with inexplicit question-answer pai
Externí odkaz:
http://arxiv.org/abs/2312.17269
Finding node correspondence across networks, namely multi-network alignment, is an essential prerequisite for joint learning on multiple networks. Despite great success in aligning networks in pairs, the literature on multi-network alignment is spars
Externí odkaz:
http://arxiv.org/abs/2310.04470
Graph Neural Networks (GNNs) have been widely applied on a variety of real-world applications, such as social recommendation. However, existing GNN-based models on social recommendation suffer from serious problems of generalization and oversmoothnes
Externí odkaz:
http://arxiv.org/abs/2304.04994
Despite the success of the Sylvester equation empowered methods on various graph mining applications, such as semi-supervised label learning and network alignment, there also exists several limitations. The Sylvester equation's inability of modeling
Externí odkaz:
http://arxiv.org/abs/2206.09477
Bundle recommendation is an emerging research direction in the recommender system with the focus on recommending customized bundles of items for users. Although Graph Neural Networks (GNNs) have been applied in this problem and achieve superior perfo
Externí odkaz:
http://arxiv.org/abs/2205.11231
Graph Neural Networks (GNNs) have achieved tremendous success in a variety of real-world applications by relying on the fixed graph data as input. However, the initial input graph might not be optimal in terms of specific downstream tasks, because of
Externí odkaz:
http://arxiv.org/abs/2205.02998
Despite the prevalence of hypergraphs in a variety of high-impact applications, there are relatively few works on hypergraph representation learning, most of which primarily focus on hyperlink prediction, often restricted to the transductive learning
Externí odkaz:
http://arxiv.org/abs/2105.10862
The past decades have witnessed the prosperity of graph mining, with a multitude of sophisticated models and algorithms designed for various mining tasks, such as ranking, classification, clustering and anomaly detection. Generally speaking, the vast
Externí odkaz:
http://arxiv.org/abs/2105.09384
Reasoning is a fundamental capability for harnessing valuable insight, knowledge and patterns from knowledge graphs. Existing work has primarily been focusing on point-wise reasoning, including search, link predication, entity prediction, subgraph ma
Externí odkaz:
http://arxiv.org/abs/2011.03189