As one of the most advanced techniques in AI, Retrieval-Augmented Generation (RAG) can
offer reliable and up-to-date external knowledge, providing huge convenience for numerous …

Recently, Retrieval-Augmented Generation (RAG) has achieved remarkable success in
addressing the challenges of Large Language Models (LLMs) without necessitating …

Deep learning, as a vital technique, has sparked a notable revolution in artificial intelligence.
As the most representative architecture, Transformers have empowered numerous …

Image restoration technology is a crucial field in image processing and is extensively utilized
across various domains. Recently, with advancements in graph convolutional network …

Knowledge representation has been a central aim of AI since its inception. Symbolic
Knowledge Graphs (KGs) and neural Large Language Models (LLMs) can both represent …

Recently, graph condensation has emerged as a prevalent technique to improve the training
efficiency for graph neural networks (GNNs). It condenses a large graph into a small one …

There is a growing interest in utilizing large-scale language models (LLMs) to advance next-
generation Recommender Systems (RecSys), driven by their outstanding language …

Given the ubiquity of graph data and its applications in diverse domains, building a Graph
Foundation Model (GFM) that can work well across different graphs and tasks with a unified …

Higher-order graph neural networks (HOGNNs) are an important class of GNN models that
harness polyadic relations between vertices beyond plain edges. They have been used to …

ABSTRACT Graph Neural Networks (GNNs) and reinforcement learning techniques are
combined in GRAPPLE (GraphSAGE Reinforced with Actor-Proximal Policy Optimization), a …