Fetching the paper…
Reading the bibliography…
Spatio-temporal prediction aims to forecast and gain insights into the ever-changing dynamics of urban environments across both time and space.
Language Models Are Few-Shot Learners. In NeurIPS . 1877–1901
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, et al · 1901
Earlier work this paper cites.
Crime rate inference with big data. In KDD . 635–644
Hongjian Wang, Daniel Kifer, Corina Graif, and Zhenhui Li. 2016 · 2016
Earlier work this paper cites.
ST-MVL: filling missing values in geo-sensory time series data. In IJCAI
Xiuwen Yi, Yu Zheng, Junbo Zhang, and Tianrui Li. 2016 · 2016
Earlier work this paper cites.
Deep Learning: A Generic Approach for Extreme Condition Traffic Forecasting. In SDM . 777–785
Rose Yu, Yaguang Li, Cyrus Shahabi, Ugur Demiryurek, and Yan Liu. 2017 · 2017
Earlier work this paper cites.
Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction. In AAAI
Junbo Zhang, Yu Zheng, and Dekang Qi. 2017 · 2017
Earlier work this paper cites.
Deepmove: Predicting human mobility with attentional recurrent networks. In WWW . 1459–1468
Jie Feng, Yong Li, Chao Zhang, Funing Sun, Fanchao Meng, Ang Guo, and Depeng Jin. 2018 · 2018
Earlier work this paper cites.
DeepCrime: Attentive hierarchical recurrent networks for crime prediction. In CIKM . 1423–1432
Chao Huang, Junbo Zhang, Yu Zheng, and Nitesh V Chawla. 2018 · 2018
Earlier work this paper cites.
Diffusion convolutional recurrent neural network: data-driven traffic forecasting. In ICLR
Yaguang Li, Rose Yu, Cyrus Shahabi, and Yan Liu. 2018 · 2018
Earlier work this paper cites.
Deep sequence learning with auxiliary information for traffic prediction. In KDD . 537–546
Binbing Liao, Jingqing Zhang, Chao Wu, Douglas McIlwraith, Tong Chen, Shengwen Yang, Yike Guo, and Fei Wu. 2018 · 2018
Earlier work this paper cites.
Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction. In AAAI
Huaxiu Yao, Fei Wu, Jintao Ke, Xianfeng Tang, Yitian Jia, Siyu Lu, Pinghua Gong, Jieping Ye, Didi Chuxing, and Zhenhui Li. 2018 · 2018
Earlier work this paper cites.
Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. In IJCAI
Bing Yu, Haoteng Yin, et al · 2018
Earlier work this paper cites.
Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting. In AAAI . 922–929
Shengnan Guo, Youfang Lin, Ning Feng, Chao Song, and Huaiyu Wan. 2019 · 2019
Earlier work this paper cites.
Urbanfm: Inferring fine-grained urban flows. In KDD . 3132–3142
Yuxuan Liang, Kun Ouyang, Lin Jing, Sijie Ruan, Ye Liu, Junbo Zhang, David S Rosenblum, and Yu Zheng. 2019 · 2019
Earlier work this paper cites.
Urban Traffic Prediction from Spatio-Temporal Data Using Deep Meta Learning. In KDD . ACM
Zheyi Pan, Yuxuan Liang, Weifeng Wang, et al · 2019
Earlier work this paper cites.
Graph wavenet for deep spatial-temporal graph modeling. In IJCAI
Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, and Chengqi Zhang. 2019 · 2019
Earlier work this paper cites.
Learning from multiple cities: A meta-learning approach for spatial-temporal prediction. In WWW . 2181–2191
Huaxiu Yao, Yiding Liu, Ying Wei, Xianfeng Tang, and Zhenhui Li. 2019 · 2019
Earlier work this paper cites.
Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting. In NeurIPS . 17804–17815
Lei Bai, Lina Yao, Can Li, Xianzhi Wang, and Can Wang. 2020 · 2020
Earlier work this paper cites.
Spatial-Temporal Synchronous Graph Convolutional Networks: A New Framework for Spatial-Temporal Network Data Forecasting. In AAAI . 914–921
Chao Song, Youfang Lin, Shengnan Guo, and Huaiyu Wan. 2020 · 2020
Cited alongside, same era.
Traffic Flow Prediction via Spatial Temporal Graph Neural Network. In WWW . 1082–1092
Xiaoyang Wang, Yao Ma, Yiqi Wang, Wei Jin, Xin Wang, Jiliang Tang, Caiyan Jia, and Jian Yu. 2020 · 2020
Cited alongside, same era.
Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks. In KDD . 753–763
Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, Xiaojun Chang, and Chengqi Zhang. 2020 · 2020
Cited alongside, same era.
T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction
Ling Zhao, Yujiao Song, Chao Zhang, Yu Liu, Pu Wang, Tao Lin, Min Deng, and Haifeng Li. 2020 · 2020
Cited alongside, same era.
GMAN: A Graph Multi-Attention Network for Traffic Prediction. In AAAI . 1234–1241
Chuanpan Zheng, Xiaoliang Fan, Cheng Wang, and Jianzhong Qi. 2020 · 2020
Cited alongside, same era.
Open-TI: Open Traffic Intelligence with Augmented Language Model
Longchao Da, Kuanru Liou, Tiejin Chen, Xuesong Zhou, Xiangyong Luo, Yezhou Yang, and Hua Wei. 2023 · 2023
Later among the works it cites.
Leveraging large language models for sequential recommendation. In Recsys . 1096–1102
Jesse Harte, Wouter Zorgdrager, Panos Louridas, Asterios Katsifodimos, Dietmar Jannach, and Marios Fragkoulis. 2023 · 2023
Later among the works it cites.
PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Prediction
Jiawei Jiang, Chengkai Han, Wayne Xin Zhao, and Jingyuan Wang. 2023 · 2023
Later among the works it cites.
GPT-ST: Generative Pre-Training of Spatio-Temporal Graph Neural Networks. In NeurIPS
Zhonghang Li, Lianghao Xia, Yong Xu, and Chao Huang. 2023 · 2023
Later among the works it cites.
Representation learning with large language models for recommendation
Xubin Ren, Wei Wei, Lianghao Xia, Lixin Su, Suqi Cheng, Junfeng Wang, Dawei Yin, and Chao Huang. 2023 · 2023
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Dynamic and Multi-Faceted Spatio-Temporal Deep Learning for Traffic Speed Forecasting. In KDD . 547–555
Liangzhe Han, Bowen Du, Leilei Sun, Yanjie Fu, Yisheng Lv, and Hui Xiong. 2021 · 2021
Cited alongside, same era.
A survey on deep learning for human mobility
Massimiliano Luca, Gianni Barlacchi, Bruno Lepri, and Luca Pappalardo. 2021 · 2021
Cited alongside, same era.
GSNet: Learning Spatial-Temporal Correlations from Geographical and Semantic Aspects for Traffic Accident Risk Forecasting
Beibei Wang, Youfang Lin, Shengnan Guo, and Huaiyu Wan. 2021b · 2021
Cited alongside, same era.
Coupled Layer-wise Graph Convolution for Transportation Demand Prediction. In AAAI . 4617–4625
Junchen Ye, Leilei Sun, Bowen Du, Yanjie Fu, and Hui Xiong. 2021 · 2021
Cited alongside, same era.
Towards Spatio- Temporal Aware Traffic Time Series Forecasting. In ICDE . 2900–2913
Razvan-Gabriel Cirstea, Bin Yang, Chenjuan Guo, Tung Kieu, and Shirui Pan. 2022 · 2022
Cited alongside, same era.
Selective cross-city transfer learning for traffic prediction via source city region re-weighting. In KDD . 731–741
Yilun Jin, Kai Chen, and Qiang Yang. 2022 · 2022
Cited alongside, same era.
Spatial-Temporal Hypergraph Self-Supervised Learning for Crime Prediction. In ICDE . 2984–2996
Zhonghang Li, Chao Huang, Lianghao Xia, Yong Xu, and Jian Pei. 2022 · 2022
Cited alongside, same era.
Later among the works it cites.
Graphgpt: Graph instruction tuning for large language models
Jiabin Tang, Yuhao Yang, Wei Wei, Lei Shi, Lixin Su, Suqi Cheng, Dawei Yin, and Chao Huang. 2023 · 2023
Later among the works it cites.
Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al · 2023
Later among the works it cites.
Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al · 2023
Later among the works it cites.
Pattern expansion and consolidation on evolving graphs for continual traffic prediction. In KDD . 2223–2232
Binwu Wang, Yudong Zhang, Xu Wang, Pengkun Wang, Zhengyang Zhou, Lei Bai, and Yang Wang. 2023 · 2023
Later among the works it cites.
Llmrec: Large language models with graph augmentation for recommendation
Wei Wei, Xubin Ren, Jiabin Tang, Qinyong Wang, Lixin Su, Suqi Cheng, Junfeng Wang, Dawei Yin, and Chao Huang. 2023 · 2023
Later among the works it cites.
GLM-130B: An Open Bilingual Pre-trained Model. In ICLR
Aohan Zeng, Xiao Liu, Zhengxiao Du, Zihan Wang, Hanyu Lai, Ming Ding, Zhuoyi Yang, Yifan Xu, Wendi Zheng, Xiao Xia, et al · 2023
Later among the works it cites.
Automated Spatio-Temporal Graph Contrastive Learning. In WWW . 295–305
Qianru Zhang, Chao Huang, Lianghao Xia, Zheng Wang, Zhonghang Li, and Siuming Yiu. 2023 · 2023
Later among the works it cites.
Dynamic Hypergraph Structure Learning for Traffic Flow Forecasting. ICDE
Yusheng Zhao, Xiao Luo, Wei Ju, Chong Chen, Xian-Sheng Hua, and Ming Zhang. 2023 · 2023
Later among the works it cites.
Judging LLM-as-a-judge with MT-Bench and Chatbot Arena
Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric. P Xing, Hao Zhang, Joseph E. Gonzalez, and Ion Stoica. 2023 · 2023
Later among the works it cites.
MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models
Deyao Zhu, Jun Chen, Xiaoqian Shen, Xiang Li, and Mohamed Elhoseiny. 2023 · 2023
Later among the works it cites.
Graph of Thoughts: Solving Elaborate Problems with Large Language Models
Maciej Besta, Nils Blach, Ales Kubicek, Robert Gerstenberger, Michal Podstawski, Lukas Gianinazzi, Joanna Gajda, Tomasz Lehmann, Hubert Niewiadomski, Piotr Nyczyk, and Torsten Hoefler. 2024 · 2024
Closest in time.
Multitask Vision-Language Prompt Tuning. In WACV . 5656–5667
Sheng Shen, Shijia Yang, Tianjun Zhang, Bohan Zhai, Joseph E. Gonzalez, Kurt Keutzer, and Trevor Darrell. 2024 · 2024
Closest in time.