28 citations · 150 across the 50 of their papers we have counts for
8 papers · 1 filter
CroTad: A Contrastive Reinforcement Learning Framework for Online Trajectory Anomaly Detection
Rui Xue, Dan He, Fengmei Jin +2
Detecting trajectory anomalies is a vital task in modern Intelligent Transportation Systems (ITS), enabling the identification of unsafe, inefficient, or irregular travel behaviour…
Zero-Shot Cellular Trajectory Map Matching
Weijie Shi, Yue Cui, Hao Chen +5
Cellular Trajectory Map-Matching (CTMM) aims to align cellular location sequences to road networks, which is a necessary preprocessing in location-based services on web platforms l…
Beyond Model Base Retrieval: Weaving Knowledge to Master Fine-grained Neural Network Design
Jialiang Wang, Hanmo Liu, Shimin Di +4
Designing high-performance neural networks for new tasks requires balancing optimization quality with search efficiency. Current methods fail to achieve this balance: neural archit…
Unraveling Spatio-Temporal Foundation Models via the Pipeline Lens: A Comprehensive Review
Yuchen Fang, Hao Miao, Yuxuan Liang +9
Spatio-temporal deep learning models aims to utilize useful patterns in such data to support tasks like prediction. However, previous deep learning models designed for specific tas…
Towards DS-NER: Unveiling and Addressing Latent Noise in Distant Annotations
Yuyang Ding, Dan Qiao, Juntao Li +4
Distantly supervised named entity recognition (DS-NER) has emerged as a cheap and convenient alternative to traditional human annotation methods, enabling the automatic generation…
Learning Towards Emergence: Paving the Way to Induce Emergence by Inhibiting Monosemantic Neurons on Pre-trained Models
Jiachuan Wang, Shimin Di, Tianhao Tang +4
Emergence, the phenomenon of a rapid performance increase once the model scale reaches a threshold, has achieved widespread attention recently. The literature has observed that mon…
A Pilot Empirical Study on When and How to Use Knowledge Graphs as Retrieval Augmented Generation
Xujie Yuan, Yongxu Liu, Shimin Di +6
The integration of Knowledge Graphs (KGs) into the Retrieval Augmented Generation (RAG) framework has attracted significant interest, with early studies showing promise in mitigati…
Top Ten Challenges Towards Agentic Neural Graph Databases
Jiaxin Bai, Zihao Wang, Yukun Zhou +16
Graph databases (GDBs) like Neo4j and TigerGraph excel at handling interconnected data but lack advanced inference capabilities. Neural Graph Databases (NGDBs) address this by inte…