Search Results for author: Yile Chen

Found 13 papers, 4 papers with code

FlexTSF: A Universal Forecasting Model for Time Series with Variable Regularities

1 code implementation30 Oct 2024 Jingge Xiao, Yile Chen, Gao Cong, Wolfgang Nejdl, Simon Gottschalk

Developing a foundation model for time series forecasting across diverse domains has attracted significant attention in recent years.

Irregular Time Series Missing Values +2

Context-Enhanced Multi-View Trajectory Representation Learning: Bridging the Gap through Self-Supervised Models

no code implementations17 Oct 2024 Tangwen Qian, Junhe Li, Yile Chen, Gao Cong, Tao Sun, Fei Wang, Yongjun Xu

To align the learning process across multiple views, we utilize GPS trajectories as a bridge and employ self-supervised pretext tasks to capture and distinguish movement patterns across different spatial views.

Representation Learning Travel Time Estimation

nextlocllm: next location prediction using LLMs

no code implementations11 Oct 2024 Shuai Liu, Ning Cao, Yile Chen, Yue Jiang, Gao Cong

Experiments show that NextLocLLM outperforms existing models in next location prediction, excelling in both supervised and zero-shot settings.

Self-supervised Learning for Geospatial AI: A Survey

no code implementations22 Aug 2024 Yile Chen, Weiming Huang, Kaiqi Zhao, Yue Jiang, Gao Cong

The proliferation of geospatial data in urban and territorial environments has significantly facilitated the development of geospatial artificial intelligence (GeoAI) across various urban applications.

Self-Supervised Learning Survey

SAGDFN: A Scalable Adaptive Graph Diffusion Forecasting Network for Multivariate Time Series Forecasting

1 code implementation18 Jun 2024 Yue Jiang, Xiucheng Li, Yile Chen, Shuai Liu, Weilong Kong, Antonis F. Lentzakis, Gao Cong

Time series forecasting is essential for our daily activities and precise modeling of the complex correlations and shared patterns among multiple time series is essential for improving forecasting performance.

Multivariate Time Series Forecasting Time Series

Road Network Representation Learning with the Third Law of Geography

no code implementations6 Jun 2024 Haicang Zhou, Weiming Huang, Yile Chen, Tiantian He, Gao Cong, Yew-Soon Ong

In response, we propose to endow road network representation with the principles of the recent Third Law of Geography.

Contrastive Learning Representation Learning

Semantic-Enhanced Representation Learning for Road Networks with Temporal Dynamics

no code implementations18 Mar 2024 Yile Chen, Xiucheng Li, Gao Cong, Zhifeng Bao, Cheng Long

In this study, we introduce a novel framework called Toast for learning general-purpose representations of road networks, along with its advanced counterpart DyToast, designed to enhance the integration of temporal dynamics to boost the performance of various time-sensitive downstream tasks.

Representation Learning

From Chaos to Clarity: Time Series Anomaly Detection in Astronomical Observations

1 code implementation15 Mar 2024 Xinli Hao, Yile Chen, Chen Yang, Zhihui Du, Chaohong Ma, Chao Wu, Xiaofeng Meng

However, existing time series anomaly detection methods fall short in tackling the unique characteristics of astronomical observations where each star is inherently independent but interfered by random concurrent noise, resulting in a high rate of false alarms.

Decoder Graph Neural Network +4

AdapTraj: A Multi-Source Domain Generalization Framework for Multi-Agent Trajectory Prediction

no code implementations22 Dec 2023 Tangwen Qian, Yile Chen, Gao Cong, Yongjun Xu, Fei Wang

However, the development of multi-source domain generalization in this task presents two notable issues: (1) negative transfer; (2) inadequate modeling for external factors.

Domain Generalization Trajectory Prediction

Points-of-Interest Relationship Inference with Spatial-enriched Graph Neural Networks

no code implementations28 Feb 2022 Yile Chen, Xiucheng Li, Gao Cong, Cheng Long, Zhifeng Bao, Shang Liu, Wanli Gu, Fuzheng Zhang

As a fundamental component in location-based services, inferring the relationship between points-of-interests (POIs) is very critical for service providers to offer good user experience to business owners and customers.

Graph Neural Network

Efficient Second-Order Optimization for Deep Learning with Kernel Machines

no code implementations29 Sep 2021 Yawen Chen, Zeyi Wen, Yile Chen, Jian Chen, Jin Huang

However, the recomputation of the Hessian matrix in the second-order optimization posts much extra computation and memory burden in the training.

Deep Learning

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