19 citations · 63 across the 15 of their papers we have counts for
15 papers
Contextual Causality with Large Language Models: A Survey
Yiheng Zhao, Jun Yan, Chengming Hu
Understanding contextual causality is critical for large language models (LLMs), as it enables them to accurately identify causal relations in specific situations and support more…
Privis: Towards Content-Aware Secure Volumetric Video Delivery
Kaiyuan Hu, Hong Kang, Yili Jin +4
Volumetric video has emerged as a key paradigm in eXtended Reality (XR) and immersive multimedia because it enables highly interactive, spatially consistent 3D experiences. However…
Prompting Wireless Networks: Reinforced In-Context Learning for Power Control
Hao Zhou, Chengming Hu, Dun Yuan +5
To manage and optimize constantly evolving wireless networks, existing machine learning (ML)- based studies operate as black-box models, leading to increased computational costs du…
Hierarchical Debate-Based Large Language Model (LLM) for Complex Task Planning of 6G Network Management
Yuyan Lin, Hao Zhou, Chengming Hu +5
6G networks have become increasingly complicated due to novel network architecture and newly emerging signal processing and transmission techniques, leading to significant burdens…
Large Language Models for Wireless Networks: An Overview from the Prompt Engineering Perspective
Hao Zhou, Chengming Hu, Dun Yuan +5
Recently, large language models (LLMs) have been successfully applied to many fields, showing outstanding comprehension and reasoning capabilities. Despite their great potential, L…
Self-Refined Generative Foundation Models for Wireless Traffic Prediction
Chengming Hu, Hao Zhou, Di Wu +3
With a broad range of emerging applications in 6G networks, wireless traffic prediction has become a critical component of network management. However, the dynamically shifting dis…
Generative AI as a Service in 6G Edge-Cloud: Generation Task Offloading by In-context Learning
Hao Zhou, Chengming Hu, Dun Yuan +5
Generative artificial intelligence (GAI) is a promising technique towards 6G networks, and generative foundation models such as large language models (LLMs) have attracted consider…
Large Language Model (LLM)-enabled In-context Learning for Wireless Network Optimization: A Case Study of Power Control
Hao Zhou, Chengming Hu, Dun Yuan +4
Large language model (LLM) has recently been considered a promising technique for many fields. This work explores LLM-based wireless network optimization via in-context learning. T…
An Overview of Machine Learning-Enabled Optimization for Reconfigurable Intelligent Surfaces-Aided 6G Networks: From Reinforcement Learning to Large Language Models
Hao Zhou, Chengming Hu, Xue Liu
Reconfigurable intelligent surface (RIS) becomes a promising technique for 6G networks by reshaping signal propagation in smart radio environments. However, it also leads to signif…
Large Language Model (LLM) for Telecommunications: A Comprehensive Survey on Principles, Key Techniques, and Opportunities
Hao Zhou, Chengming Hu, Ye Yuan +11
Large language models (LLMs) have received considerable attention recently due to their outstanding comprehension and reasoning capabilities, leading to great progress in many fiel…
Less or More From Teacher: Exploiting Trilateral Geometry For Knowledge Distillation
Chengming Hu, Haolun Wu, Xuan Li +5
Knowledge distillation aims to train a compact student network using soft supervision from a larger teacher network and hard supervision from ground truths. However, determining an…
Teacher-Student Architecture for Knowledge Distillation: A Survey
Chengming Hu, Xuan Li, Dan Liu +4
Although Deep neural networks (DNNs) have shown a strong capacity to solve large-scale problems in many areas, such DNNs are hard to be deployed in real-world systems due to their…
Phase Matching for Out-of-Distribution Generalization
Chengming Hu, Yeqian Du, Rui Wang +2
The Fourier transform, an explicit decomposition method for visual signals, has been employed to explain the out-of-distribution generalization behaviors of Deep Neural Networks (D…
Teacher-Student Architecture for Knowledge Learning: A Survey
Chengming Hu, Xuan Li, Dan Liu +3
Although Deep Neural Networks (DNNs) have shown a strong capacity to solve large-scale problems in many areas, such DNNs with voluminous parameters are hard to be deployed in a rea…
Encoder-Decoder Architecture for Supervised Dynamic Graph Learning: A Survey
Yuecai Zhu, Fuyuan Lyu, Chengming Hu +2
In recent years, the prevalent online services generate a sheer volume of user activity data. Service providers collect these data in order to perform client behavior analysis, and…