most citedAn Interactive Multi-modal Query Answering System with Retrieval-Augmented Large Language Models

2 citations · 3 across the 5 of their papers we have counts for

collaborators

5 papers

cs.DB20242 cited

An Interactive Multi-modal Query Answering System with Retrieval-Augmented Large Language Models

Mengzhao Wang, Haotian Wu, Xiangyu Ke +3

Retrieval-augmented Large Language Models (LLMs) have reshaped traditional query-answering systems, offering unparalleled user experiences. However, existing retrieval techniques o…

cs.IT2024

MIMO Channel as a Neural Function: Implicit Neural Representations for Extreme CSI Compression in Massive MIMO Systems

Haotian Wu, Maojun Zhang, Yulin Shao +2

Acquiring and utilizing accurate channel state information (CSI) can significantly improve transmission performance, thereby holding a crucial role in realizing the potential advan…

cs.IR2023

TDCGL: Two-Level Debiased Contrastive Graph Learning for Recommendation

Yubo Gao, Haotian Wu

knowledge graph-based recommendation methods have achieved great success in the field of recommender systems. However, over-reliance on high-quality knowledge graphs is a bottlenec…

eess.IV20231 cited

Features-over-the-Air: Contrastive Learning Enabled Cooperative Edge Inference

Haotian Wu, Nitish Mital, Krystian Mikolajczyk +1

We study the collaborative image retrieval problem at the wireless edge, where multiple edge devices capture images of the same object, which are then used jointly to retrieve simi…

eess.IV2023

Collaborative Semantic Communication for Edge Inference

Wing Fei Lo, Nitish Mital, Haotian Wu +1

We study the collaborative image retrieval problem at the wireless edge, where multiple edge devices capture images of the same object from different angles and locations, which ar…