Knowledge Graph Large Language Model (KG-LLM) for Link Prediction

no code implementations • 12 Mar 2024 • Dong Shu, Tianle Chen, Mingyu Jin, Yiting Zhang, Chong Zhang, Mengnan Du, Yongfeng Zhang

The task of predicting multiple links within knowledge graphs (KGs) stands as a challenge in the field of knowledge graph analysis, a challenge increasingly resolvable due to advancements in natural language processing (NLP) and KG embedding techniques.

In-Context Learning Knowledge Graphs +3

Bayesian Federated Learning Via Expectation Maximization and Turbo Deep Approximate Message Passing

no code implementations • 12 Feb 2024 • Wei Xu, An Liu, Yiting Zhang, Vincent Lau

In this work, we propose a message passing based Bayesian federated learning (BFL) framework to avoid these drawbacks. Specifically, we formulate the problem of deep neural network (DNN) learning and compression and as a sparse Bayesian inference problem, in which group sparse prior is employed to achieve structured model compression.

Bayesian Inference Federated Learning +3