Knowledge Adaption for Demand Prediction based on Multi-task Memory Neural Network
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Wei Liu | Lei Bai | Can Li | Lina Yao | S Travis Waller | Lina Yao | S. Waller | Wei Liu | Lei Bai | Can Li
[1] Linpeng Huang,et al. Predicting Multi-step Citywide Passenger Demands Using Attention-based Neural Networks , 2018, WSDM.
[2] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[3] Max Welling,et al. Semi-Supervised Classification with Graph Convolutional Networks , 2016, ICLR.
[4] Paolo Frasconi,et al. Short-Term Traffic Flow Forecasting: An Experimental Comparison of Time-Series Analysis and Supervised Learning , 2013, IEEE Transactions on Intelligent Transportation Systems.
[5] Feng Liu,et al. Cross-City Transfer Learning for Deep Spatio-Temporal Prediction , 2018, IJCAI.
[6] Xavier Bresson,et al. Structured Sequence Modeling with Graph Convolutional Recurrent Networks , 2016, ICONIP.
[7] João Gama,et al. Predicting Taxi–Passenger Demand Using Streaming Data , 2013, IEEE Transactions on Intelligent Transportation Systems.
[8] Xianfeng Tang,et al. Learning from Multiple Cities: A Meta-Learning Approach for Spatial-Temporal Prediction , 2019, WWW.
[9] Zheng Wang,et al. Learning to Estimate the Travel Time , 2018, KDD.
[10] Xuanjing Huang,et al. Recurrent Neural Network for Text Classification with Multi-Task Learning , 2016, IJCAI.
[11] Guokun Lai,et al. Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks , 2017, SIGIR.
[12] Xuanjing Huang,et al. Deep Multi-Task Learning with Shared Memory for Text Classification , 2016, EMNLP.
[13] Kai Zheng,et al. Origin-Destination Matrix Prediction via Graph Convolution: a New Perspective of Passenger Demand Modeling , 2019, KDD.
[14] Jieping Ye,et al. Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction , 2018, AAAI.
[15] Simon Haykin,et al. GradientBased Learning Applied to Document Recognition , 2001 .
[16] Jun Xu,et al. Real-Time Prediction of Taxi Demand Using Recurrent Neural Networks , 2018, IEEE Transactions on Intelligent Transportation Systems.
[17] Lina Yao,et al. Passenger Demand Forecasting with Multi-Task Convolutional Recurrent Neural Networks , 2019, PAKDD.
[18] Ao Tang,et al. DSANet: Dual Self-Attention Network for Multivariate Time Series Forecasting , 2019, CIKM.
[19] Garrison W. Cottrell,et al. A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction , 2017, IJCAI.
[20] Lina Yao,et al. STG2Seq: Spatial-temporal Graph to Sequence Model for Multi-step Passenger Demand Forecasting , 2019, IJCAI.
[21] Yu Zheng,et al. Deep Distributed Fusion Network for Air Quality Prediction , 2018, KDD.
[22] Tianqi Chen,et al. XGBoost: A Scalable Tree Boosting System , 2016, KDD.
[23] Qiang Yang,et al. Transfer Knowledge between Cities , 2016, KDD.
[24] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[25] Philip S. Yu,et al. Memory in Memory: A Predictive Neural Network for Learning Higher-Order Non-Stationarity From Spatiotemporal Dynamics , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[26] Alex Graves,et al. Scaling Memory-Augmented Neural Networks with Sparse Reads and Writes , 2016, NIPS.
[27] Chenliang Li,et al. A Deep Neural Framework for Sales Forecasting in E-Commerce , 2019, CIKM.
[28] Bin Yang,et al. Correlated Time Series Forecasting using Multi-Task Deep Neural Networks , 2018, CIKM.
[29] Rui Xue,et al. Short-Term Bus Passenger Demand Prediction Based on Time Series Model and Interactive Multiple Model Approach , 2015 .
[30] Lina Yao,et al. Spatio-Temporal Graph Convolutional and Recurrent Networks for Citywide Passenger Demand Prediction , 2019, CIKM.
[31] Yanjie Fu,et al. Co-Prediction of Multiple Transportation Demands Based on Deep Spatio-Temporal Neural Network , 2019, KDD.
[32] Cyrus Shahabi,et al. Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting , 2017, ICLR.