【论文笔记】A Deep Learning Approach to the Citywide Traffic Accident Risk Prediction
【论文笔记】A Deep Learning Approach to the Citywide Traffic Accident Risk Prediction 问题: 具有高时空分辨率的交通事故风险预测困难 复杂的交通环境， 人类行为 缺乏实时的交通相关数据。 交通事故的动态性 本文成果 本文收集了重大交通事故数据，建立了基于循环神经网络的交通事故风险预测深度模型。通过分析交通事故频发的时空模式，
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相关文章
- 1. 论文笔记：Learning how to Active Learn: A Deep Reinforcement Learning Approach
- 2. On the prediction of DNA-binding proteins only from primary sequences: A deep learning approach论文笔记
- 3. LC-RNN: A Deep Learning Model for Traffic Speed Prediction
- 4. 【AAAI 2020】RiskOracle: A Minute-level Citywide Traffic Accident Forecasting Framework
- 5. 论文笔记（一）Heterogeneous Face Attribute Estimation: A Deep Multi-Task Learning Approach
- 6. 【论文笔记】PassGAN: A Deep Learning Approach for Password Guessing
- 7. 【论文笔记】Deep Learning on Graphs: A Survey
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- 10. 《A Discriminative Feature Learning Approach for Deep Face Recognition》论文笔记