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20232026
most citedBalancing Specialized and General Skills in LLMs: The Impact of Modern Tuning and Data Strategy

12 citations · 29 across the 16 of their papers we have counts for

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8 papers · 1 filter

cs.LG2026

Staypoint Detection from Noisy Trajectory Data [Experiment Paper]

Lance Kennedy, Hossein Amiri, Yueyang Liu +9

Detecting staypoints from raw trajectory data is fundamental to numerous spatial computing applications. This process transforms raw numeric sequences of geolocations into semantic…

cs.LG2024

Transferable Unsupervised Outlier Detection Framework for Human Semantic Trajectories

Zheng Zhang, Hossein Amiri, Dazhou Yu +3

Semantic trajectories, which enrich spatial-temporal data with textual information such as trip purposes or location activities, are key for identifying outlier behaviors critical…

cs.LG2024

Representation Learning of Geometric Trees

Zheng Zhang, Allen Zhang, Ruth Nelson +2

Geometric trees are characterized by their tree-structured layout and spatially constrained nodes and edges, which significantly impacts their topological attributes. This inherent…

cs.LG2024

TAGA: Text-Attributed Graph Self-Supervised Learning by Synergizing Graph and Text Mutual Transformations

Zheng Zhang, Yuntong Hu, Bo Pan +2

Text-Attributed Graphs (TAGs) enhance graph structures with natural language descriptions, enabling detailed representation of data and their relationships across a broad spectrum…

cs.LG2024★ 10 cited

GRAG: Graph Retrieval-Augmented Generation

Yuntong Hu, Zhihan Lei, Zheng Zhang +3

Naive Retrieval-Augmented Generation (RAG) focuses on individual documents during retrieval and, as a result, falls short in handling networked documents which are very popular in…

cs.LG2023

Transferable Deep Clustering Model

Zheng Zhang, Liang Zhao

Deep learning has shown remarkable success in the field of clustering recently. However, how to transfer a trained clustering model on a source domain to a target domain by leverag…

cs.LG2023★ 2 cited

Beyond Text: A Deep Dive into Large Language Models' Ability on Understanding Graph Data

Yuntong Hu, Zheng Zhang, Liang Zhao

Large language models (LLMs) have achieved impressive performance on many natural language processing tasks. However, their capabilities on graph-structured data remain relatively…

cs.LG2023★ 1 cited

Large Language Models for Spatial Trajectory Patterns Mining

Zheng Zhang, Hossein Amiri, Zhenke Liu +2

Identifying anomalous human spatial trajectory patterns can indicate dynamic changes in mobility behavior with applications in domains like infectious disease monitoring and elderl…