Search Results for author:
Found 14 papers, 6 papers with code
OverleafCopilot: Empowering Academic Writing in Overleaf with Large Language Models
In this technical report, we explore the integration of LLMs and the popular academic writing tool, Overleaf, to enhance the efficiency and quality of academic writing.
Diff-RNTraj: A Structure-aware Diffusion Model for Road Network-constrained Trajectory Generation
In this paper, we propose a new problem to meet the practical application need, \emph{i. e.}, road network-constrained trajectory (RNTraj) generation, which can directly generate trajectories on the road network with road-related information.
UVTM: Universal Vehicle Trajectory Modeling with ST Feature Domain Generation
However, most methods target only one specific task and cannot be applied universally.
Local-Global History-aware Contrastive Learning for Temporal Knowledge Graph Reasoning
Temporal knowledge graphs (TKGs) have been identified as a promising approach to represent the dynamics of facts along the timeline.
Towards Enhancing Relational Rules for Knowledge Graph Link Prediction
To address these issues, we propose a novel knowledge graph reasoning approach, the Relational rUle eNhanced Graph Neural Network (RUN-GNN).
A Survey on Service Route and Time Prediction in Instant Delivery: Taxonomy, Progress, and Prospects
An emerging research area within these services is service Route\&Time Prediction (RTP), which aims to estimate the future service route as well as the arrival time of a given worker.
DRL4Route: A Deep Reinforcement Learning Framework for Pick-up and Delivery Route Prediction
Deep neural networks based on supervised learning have emerged as the dominant model for the task because of their powerful ability to capture workers' behavior patterns from massive historical data.
Origin-Destination Travel Time Oracle for Map-based Services
Given an origin (O), a destination (D), and a departure time (T), an Origin-Destination (OD) travel time oracle~(ODT-Oracle) returns an estimate of the time it takes to travel from O to D when departing at T. ODT-Oracles serve important purposes in map-based services.
LaDe: The First Comprehensive Last-mile Delivery Dataset from Industry
In this paper, we introduce \texttt{LaDe}, the first publicly available last-mile delivery dataset with millions of packages from the industry.
DiffSTG: Probabilistic Spatio-Temporal Graph Forecasting with Denoising Diffusion Models
Spatio-temporal graph neural networks (STGNN) have emerged as the dominant model for spatio-temporal graph (STG) forecasting.
Pre-training General Trajectory Embeddings with Maximum Multi-view Entropy Coding
Spatio-temporal trajectories provide valuable information about movement and travel behavior, enabling various downstream tasks that in turn power real-world applications.
Spatial-Temporal Synchronous Graph Convolutional Networks: A New Framework for Spatial-Temporal Network Data Forecasting
Spatial-temporal network data forecasting is of great importance in a huge amount of applications for traffic management and urban planning.
Ranked #3 on
Traffic Prediction
on BJTaxi
Improving Relation Classification by Entity Pair Graph
Existing relation classification approaches mainly rely on exploiting external resources and background knowledge to improve the performance and ignore the correlations between entity pairs which are helpful for relation classification.
Ranked #10 on
Relation Extraction
on SemEval-2010 Task-8
Exploiting the Entity Type Sequence to Benefit Event Detection
Event Detection (ED) is one of the most important task in the field of information extraction.
