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pro vyhledávání: '"Zhang, Haimin"'
Large language models (LLMs) and retrieval-augmented generation (RAG) techniques have revolutionized traditional information access, enabling AI agent to search and summarize information on behalf of users during dynamic dialogues. Despite their pote
Externí odkaz:
http://arxiv.org/abs/2409.00636
Modeling feature interactions is crucial for click-through rate (CTR) prediction, particularly when it comes to high-order explicit interactions. Traditional methods struggle with this task because they often predefine a maximum interaction order, wh
Externí odkaz:
http://arxiv.org/abs/2408.08713
Object parts serve as crucial intermediate representations in various downstream tasks, but part-level representation learning still has not received as much attention as other vision tasks. Previous research has established that Vision Transformer c
Externí odkaz:
http://arxiv.org/abs/2408.08108
Retrieval-augmented generation (RAG) techniques leverage the in-context learning capabilities of large language models (LLMs) to produce more accurate and relevant responses. Originating from the simple 'retrieve-then-read' approach, the RAG framewor
Externí odkaz:
http://arxiv.org/abs/2407.10670
Combining the message-passing paradigm with the global attention mechanism has emerged as an effective framework for learning over graphs. The message-passing paradigm and the global attention mechanism fundamentally generate node embeddings based on
Externí odkaz:
http://arxiv.org/abs/2407.02758
Graph neural networks (GNNs) have shown great success in learning from graph-based data. The key mechanism of current GNNs is message passing, where a node's feature is updated based on the information passing from its local neighbourhood. A limitati
Externí odkaz:
http://arxiv.org/abs/2405.04755
Retrieval-augmented generation (RAG) for language models significantly improves language understanding systems. The basic retrieval-then-read pipeline of response generation has evolved into a more extended process due to the integration of various c
Externí odkaz:
http://arxiv.org/abs/2405.06683
Autor:
Zhang, Haimin, Xu, Min
Message passing has become the dominant framework in graph representation learning. The essential idea of the message-passing framework is to update node embeddings based on the information aggregated from local neighbours. However, most existing agg
Externí odkaz:
http://arxiv.org/abs/2404.09809
Autor:
Zhang, Haimin, Xu, Min
Studies continually find that message-passing graph convolutional networks suffer from the over-smoothing issue. Basically, the issue of over-smoothing refers to the phenomenon that the learned embeddings for all nodes can become very similar to one
Externí odkaz:
http://arxiv.org/abs/2404.09774
Contact-free vital sign monitoring, which uses wireless signals for recognizing human vital signs (i.e, breath and heartbeat), is an attractive solution to health and security. However, the subject's body movement and the change in actual environment
Externí odkaz:
http://arxiv.org/abs/2304.11057