1.Python Learning Manual (4th Edition).pdf
2.TensorFlow in practice
3. Didi " Learning to Estimate the Travel Time", wide+deep+lstm time series forecasting
4. Multi-features taxi destination prediction with frequency domain processing, trajectory dimension reduction grid, and then extracting grid features through CNN
5.Deep spatio-temporal residual networks for citywide crowd flows prediction cnn timeliness prediction
6.Artificial Neural Networks Applied to Taxi Destination Prediction.pdf
7.Learning Graph-based POI Embedding for Location-based Recommendation
8.T-CONV- A Convolutional Neural Network For Multi-scale Taxi Trajectory Prediction
9.Maximizing Intervention Effectiveness
10.How Do Price Promotions Affect Customer Behavior on Retailing Platforms? Evidence from a Large Randomized Experiment on Alibaba Dennis J. Zhang
11.Taking Assortment Optimization from Theory to Practice: Evidence from Large Field Experiments on Alibaba
12.Solving shortage in a priceless market: Insights from blood donation
13.Can Labor Regulation Hinder Economic Performance? Evidence from India1 Timothy Besley and Robin Burgess
14.【deepmind】Neural Combinatorial Optimization with Reinforcement Learning
15.【deepmind】Playing Atari with Deep Reinforcement Learning
16.【ICLR2015】Move evaluation in go using deep convolutional neural networks
17.【icml2007】Combining online and offline knowledge in UCT
18.【ICML2017 Best Paper】Understanding Black-box Predictions via Influence Function
19.【kdd2016】Rebalancing Bike Sharing Systems A Multi-source
20.【kdd2017 alibaba】Local Algorithm for User Action Prediction Towards Display Ads
21.【kdd2017 滴滴】The Simpler The Better A Unified Approach to Predicting Original Taxi Demands on Large-Scale Online Platforms
22. [kdd2017 Didi Dispatch Combination Optimization] A Taxi Order Dispatch Model based On Combinatorial Optimization
23.【kdd2018】Efficient Large-Scale Fleet Management via Multi-Agent Deep Reinforcement Learning
24.【kdd2018】KDD2018dynamic-bike-reposition
25.【kdd2018】Large-Scale Order Dispatch in On-Demand Ride-Sharing Platforms A Learning and Planning Approach
26.【kdd2018】Multi-task Representation Learning for Travel Time Estimation
27.【kdd2018】Trajectory-driven Influential Billboard Placement
28.【nature】Human-level control through deep reinforcement
29.【nature】Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
30.【nature】Mastering the Game of Go with Deep Neural Networks and
31.【nature】Mastering the Game of Go without Human Knowledge
32.A Hybrid Framework for Text Modeling with Convolutional RNN
33.Deep Embedding Forest_ Forest-based Serving with Deep Embedding Features
34.ICML2017 Deep Reinforcement Learning, Decision
35.Planning Bike Lanes based on Sharing-Bikes’ Trajectories
36.Learning to branch in mixed integer programming
37.AI Buzzword Explained Multi-Agent Path Finding (MAPF)
38.Wide & Deep Learning for Recommender Systems
39.【zhengyu】Effective and Efficient Large-scale Dynamic City Express
40.【KDD2018】dynamic-bike-reposition
41.【KDD2017】Planning Bike Lanes based on Sharing-Bikes’ Trajectories
42.【KDD2016】Rebalancing Bike Sharing Systems A Multi-source
43.SmartAdP: Visual Analytics of Large-scale Taxi Trajectories for Selecting Billboard Locations
44.k facility location problem
45.Machine learning for global optimization
46.Order Dispatch in Priceaware Ridesharing
47.Spatial Crowdsourcing: Challenges and Opportunities
48.TrajectoryCoverage_GIS2016_Zheng-1
49.UrbanComputing-zheng-tist2014
50 Dragonfly gbdt