Search Results for author: Renhe Jiang

Found 42 papers, 30 papers with code

LAG: LLM agents for Leaderboard Auto Generation on Demanding

no code implementations25 Feb 2025 Jian Wu, Jiayu Zhang, Dongyuan Li, Linyi Yang, Aoxiao Zhong, Renhe Jiang, Qingsong Wen, Yue Zhang

This paper introduces Leaderboard Auto Generation (LAG), a novel and well-organized framework for automatic generation of leaderboards on a given research topic in rapidly evolving fields like Artificial Intelligence (AI).

Document Summarization Multi-Document Summarization

Multi-Agent Autonomous Driving Systems with Large Language Models: A Survey of Recent Advances

no code implementations24 Feb 2025 Yaozu Wu, Dongyuan Li, Yankai Chen, Renhe Jiang, Henry Peng Zou, Liancheng Fang, Zhen Wang, Philip S. Yu

Autonomous Driving Systems (ADSs) are revolutionizing transportation by reducing human intervention, improving operational efficiency, and enhancing safety.

Autonomous Driving Decision Making

Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting

1 code implementation18 Nov 2024 Hongjun Wang, Jiyuan Chen, Lingyu Zhang, Renhe Jiang, Xuan Song

To address this limitation, we reconsider the design of adaptive embeddings and propose a Principal Component Analysis (PCA) embedding approach that enables models to adapt to new scenarios without retraining.

Attribute Traffic Prediction

Instruction-Tuning Llama-3-8B Excels in City-Scale Mobility Prediction

1 code implementation31 Oct 2024 Peizhi Tang, Chuang Yang, Tong Xing, Xiaohang Xu, Renhe Jiang, Kaoru Sezaki

Human mobility prediction plays a critical role in applications such as disaster response, urban planning, and epidemic forecasting.

Disaster Response Language Modeling +4

Extracting Spatiotemporal Data from Gradients with Large Language Models

no code implementations21 Oct 2024 Lele Zheng, Yang Cao, Renhe Jiang, Kenjiro Taura, Yulong Shen, Sheng Li, Masatoshi Yoshikawa

To understand privacy risks in spatiotemporal federated learning, we first propose Spatiotemporal Gradient Inversion Attack (ST-GIA), a gradient attack algorithm tailored to spatiotemporal data that successfully reconstructs the original location from gradients.

Federated Learning Language Modelling

Taming the Long Tail in Human Mobility Prediction

1 code implementation19 Oct 2024 Xiaohang Xu, Renhe Jiang, Chuang Yang, Zipei Fan, Kaoru Sezaki

With the popularity of location-based services, human mobility prediction plays a key role in enhancing personalized navigation, optimizing recommendation systems, and facilitating urban mobility and planning.

Prediction Recommendation Systems

SIMformer: Single-Layer Vanilla Transformer Can Learn Free-Space Trajectory Similarity

no code implementations18 Oct 2024 Chuang Yang, Renhe Jiang, Xiaohang Xu, Chuan Xiao, Kaoru Sezaki

Free-space trajectory similarity calculation, e. g., DTW, Hausdorff, and Frechet, often incur quadratic time complexity, thus learning-based methods have been proposed to accelerate the computation.

Towards Neural Scaling Laws for Time Series Foundation Models

1 code implementation16 Oct 2024 Qingren Yao, Chao-Han Huck Yang, Renhe Jiang, Yuxuan Liang, Ming Jin, Shirui Pan

In this work, we examine two common TSFM architectures, encoder-only and decoder-only Transformers, and investigate their scaling behavior on both ID and OOD data.

Decoder Time Series

Diffusion Models in 3D Vision: A Survey

no code implementations7 Oct 2024 Zhen Wang, Dongyuan Li, Yaozu Wu, Tianyu He, Jiang Bian, Renhe Jiang

In recent years, 3D vision has become a crucial field within computer vision, powering a wide range of applications such as autonomous driving, robotics, augmented reality, and medical imaging.

Autonomous Driving Computational Efficiency +3

Robust Traffic Forecasting against Spatial Shift over Years

1 code implementation1 Oct 2024 Hongjun Wang, Jiyuan Chen, Tong Pan, Zheng Dong, Lingyu Zhang, Renhe Jiang, Xuan Song

Through meticulous analysis, we attribute this decline to the models' inability to adapt to previously unobserved spatial relationships.

Attribute

STGformer: Efficient Spatiotemporal Graph Transformer for Traffic Forecasting

1 code implementation1 Oct 2024 Hongjun Wang, Jiyuan Chen, Tong Pan, Zheng Dong, Lingyu Zhang, Renhe Jiang, Xuan Song

STGformer effectively balances the strengths of GCNs and Transformers, enabling efficient modeling of both global and local traffic patterns while maintaining a manageable computational footprint.

Non-Homophilic Graph Pre-Training and Prompt Learning

1 code implementation22 Aug 2024 Xingtong Yu, Jie Zhang, Yuan Fang, Renhe Jiang

In particular, many real-world graphs are non-homophilic, not strictly or uniformly homophilic with mixing homophilic and heterophilic patterns, exhibiting varying non-homophilic characteristics across graphs and nodes.

DyG-Mamba: Continuous State Space Modeling on Dynamic Graphs

no code implementations13 Aug 2024 Dongyuan Li, Shiyin Tan, Ying Zhang, Ming Jin, Shirui Pan, Manabu Okumura, Renhe Jiang

Dynamic graph learning aims to uncover evolutionary laws in real-world systems, enabling accurate social recommendation (link prediction) or early detection of cancer cells (classification).

Dynamic Link Prediction Dynamic Node Classification +5

Enhancing Spatio-temporal Quantile Forecasting with Curriculum Learning: Lessons Learned

no code implementations18 Jun 2024 Du Yin, Jinliang Deng, Shuang Ao, Zechen Li, Hao Xue, Arian Prabowo, Renhe Jiang, Xuan Song, Flora Salim

Furthermore, our framework incorporates a stacking fusion module to combine diverse information from three types of curriculum learning, resulting in a strong and thorough learning process.

Continuous Temporal Domain Generalization

1 code implementation25 May 2024 Zekun Cai, Guangji Bai, Renhe Jiang, Xuan Song, Liang Zhao

Temporal Domain Generalization (TDG) addresses the challenge of training predictive models under temporally varying data distributions.

Domain Generalization

Multi-Modality Spatio-Temporal Forecasting via Self-Supervised Learning

1 code implementation6 May 2024 Jiewen Deng, Renhe Jiang, JiaQi Zhang, Xuan Song

Multi-modality spatio-temporal (MoST) data extends spatio-temporal (ST) data by incorporating multiple modalities, which is prevalent in monitoring systems, encompassing diverse traffic demands and air quality assessments.

Self-Supervised Learning Spatio-Temporal Forecasting

Community-Invariant Graph Contrastive Learning

1 code implementation2 May 2024 Shiyin Tan, Dongyuan Li, Renhe Jiang, Ying Zhang, Manabu Okumura

Graph augmentation has received great attention in recent years for graph contrastive learning (GCL) to learn well-generalized node/graph representations.

Contrastive Learning

Large Language Models as Urban Residents: An LLM Agent Framework for Personal Mobility Generation

2 code implementations22 Feb 2024 Jiawei Wang, Renhe Jiang, Chuang Yang, Zengqing Wu, Makoto Onizuka, Ryosuke Shibasaki, Noboru Koshizuka, Chuan Xiao

This paper introduces a novel approach using Large Language Models (LLMs) integrated into an agent framework for flexible and effective personal mobility generation.

Retrieval

Spatial-Temporal-Decoupled Masked Pre-training for Spatiotemporal Forecasting

1 code implementation1 Dec 2023 Haotian Gao, Renhe Jiang, Zheng Dong, Jinliang Deng, Yuxin Ma, Xuan Song

Spatiotemporal forecasting techniques are significant for various domains such as transportation, energy, and weather.

 Ranked #1 on Traffic Prediction on EXPY-TKY (using extra training data)

Time Series Traffic Prediction

Revisiting Mobility Modeling with Graph: A Graph Transformer Model for Next Point-of-Interest Recommendation

1 code implementation2 Oct 2023 Xiaohang Xu, Toyotaro Suzumura, Jiawei Yong, Masatoshi Hanai, Chuang Yang, Hiroki Kanezashi, Renhe Jiang, Shintaro Fukushima

Extracting distinct fine-grained features unique to each piece of information is difficult since temporal information often includes spatial information, as users tend to visit nearby POIs.

MemDA: Forecasting Urban Time Series with Memory-based Drift Adaptation

2 code implementations25 Sep 2023 Zekun Cai, Renhe Jiang, Xinyu Yang, Zhaonan Wang, Diansheng Guo, Hiroki Kobayashi, Xuan Song, Ryosuke Shibasaki

Urban time series data forecasting featuring significant contributions to sustainable development is widely studied as an essential task of the smart city.

Multivariate Time Series Forecasting Time Series +2

STAEformer: Spatio-Temporal Adaptive Embedding Makes Vanilla Transformer SOTA for Traffic Forecasting

1 code implementation21 Aug 2023 Hangchen Liu, Zheng Dong, Renhe Jiang, Jiewen Deng, Jinliang Deng, Quanjun Chen, Xuan Song

With the rapid development of the Intelligent Transportation System (ITS), accurate traffic forecasting has emerged as a critical challenge.

Time Series Traffic Prediction

Learning Gaussian Mixture Representations for Tensor Time Series Forecasting

1 code implementation1 Jun 2023 Jiewen Deng, Jinliang Deng, Renhe Jiang, Xuan Song

Tensor time series (TTS) data, a generalization of one-dimensional time series on a high-dimensional space, is ubiquitous in real-world scenarios, especially in monitoring systems involving multi-source spatio-temporal data (e. g., transportation demands and air pollutants).

Representation Learning Time Series +1

MegaCRN: Meta-Graph Convolutional Recurrent Network for Spatio-Temporal Modeling

1 code implementation12 Dec 2022 Renhe Jiang, Zhaonan Wang, Jiawei Yong, Puneet Jeph, Quanjun Chen, Yasumasa Kobayashi, Xuan Song, Toyotaro Suzumura, Shintaro Fukushima

Spatio-temporal modeling as a canonical task of multivariate time series forecasting has been a significant research topic in AI community.

Decoder Graph Learning +4

Easy Begun is Half Done: Spatial-Temporal Graph Modeling with ST-Curriculum Dropout

2 code implementations28 Nov 2022 Hongjun Wang, Jiyuan Chen, Tong Pan, Zipei Fan, Boyuan Zhang, Renhe Jiang, Lingyu Zhang, Yi Xie, Zhongyi Wang, Xuan Song

Spatial-temporal (ST) graph modeling, such as traffic speed forecasting and taxi demand prediction, is an important task in deep learning area.

Spatio-Temporal Meta-Graph Learning for Traffic Forecasting

1 code implementation27 Nov 2022 Renhe Jiang, Zhaonan Wang, Jiawei Yong, Puneet Jeph, Quanjun Chen, Yasumasa Kobayashi, Xuan Song, Shintaro Fukushima, Toyotaro Suzumura

Traffic forecasting as a canonical task of multivariate time series forecasting has been a significant research topic in AI community.

Decoder Graph Learning +4

Online Trajectory Prediction for Metropolitan Scale Mobility Digital Twin

no code implementations21 Jun 2022 Zipei Fan, Xiaojie Yang, Wei Yuan, Renhe Jiang, Quanjun Chen, Xuan Song, Ryosuke Shibasaki

In the first stage, to encode the daily variation of human mobility at a metropolitan level, we automatically extract citywide mobility trends as crowd contexts and predict long-term and long-distance movements at a coarse level.

Prediction Retrieval +1

Event-Aware Multimodal Mobility Nowcasting

1 code implementation14 Dec 2021 Zhaonan Wang, Renhe Jiang, Hao Xue, Flora D. Salim, Xuan Song, Ryosuke Shibasaki

As a decisive part in the success of Mobility-as-a-Service (MaaS), spatio-temporal predictive modeling for crowd movements is a challenging task particularly considering scenarios where societal events drive mobility behavior deviated from the normality.

Spatio-Temporal-Categorical Graph Neural Networks for Fine-Grained Multi-Incident Co-Prediction

1 code implementation CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management 2021 Zhaonan Wang, Renhe Jiang, Zekun Cai, Zipei Fan, Xin Liu, Kyoung-Sook Kim, Xuan Song, Ryosuke Shibasaki

Forecasting incident occurrences (e. g. crime, EMS, traffic accident) is a crucial task for emergency service providers and transportation agencies in performing response time optimization and dynamic fleet management.

Decision Making Management +1

A Multi-view Multi-task Learning Framework for Multi-variate Time Series Forecasting

1 code implementation2 Sep 2021 Jinliang Deng, Xiusi Chen, Renhe Jiang, Xuan Song, Ivor W. Tsang

Therefore, there are two fundamental views which can be used to analyze MTS data, namely the spatial view and the temporal view.

Attribute Multi-Task Learning +2

DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic Prediction

3 code implementations20 Aug 2021 Renhe Jiang, Du Yin, Zhaonan Wang, Yizhuo Wang, Jiewen Deng, Hangchen Liu, Zekun Cai, Jinliang Deng, Xuan Song, Ryosuke Shibasaki

Nowadays, with the rapid development of IoT (Internet of Things) and CPS (Cyber-Physical Systems) technologies, big spatiotemporal data are being generated from mobile phones, car navigation systems, and traffic sensors.

Deep Learning Time Series +2

VLUC: An Empirical Benchmark for Video-Like Urban Computing on Citywide Crowd and Traffic Prediction

no code implementations16 Nov 2019 Renhe Jiang, Zekun Cai, Zhaonan Wang, Chuang Yang, Zipei Fan, Xuan Song, Kota Tsubouchi, Ryosuke Shibasaki

In this study, we publish a new aggregated human mobility dataset generated from a real-world smartphone application and build a standard benchmark for such kind of video-like urban computing with this new dataset and the existing open datasets.

Management Traffic Prediction

DeepUrbanEvent: A System for Predicting Citywide Crowd Dynamics at Big Events

1 code implementation 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining 2019 Renhe Jiang, Xuan Song, Dou Huang, Xiaoya Song, Tianqi Xia, Zekun Cai, Zhaonan Wang, Kyoung-Sook Kim, Ryosuke Shibasaki

Therefore in this study, we aim to extract the “deep” trend only from the current momentary observations and generate an accurate prediction for the trend in the short future, which is considered to be an effective way to deal with the event situations.

Management Prediction +1

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