2022/03/09

• Attendee: Zhiyuan, Xiangyu, Yang
• Meeting Summary:
  • Zhiyuan: Share the LSTM+GCN overfitting problems, Yang gives some advice: 1. Visualize the correlation about the close points in the graph. 2.
  • Xiangyu: Show the results about META-MLP and MLP in UberNYC. Yang gives advice: 1. Divide the task into weekdays and weekends looks like a cross-domain problem. In cross-domain problems, the pre-training method is often better than meta-learning, share the article about the cross-domain. 2. Change the backbone network such as GCN to see the results.
  • Yang: Communicate with the Gaode group once every two weeks.

• To Do:
  • Zhiyuan and Yuanbo: 1. Visualize the graph model prediction data and compare it with the ground truth using the hot map. 2. Pre-training the LSTM then add the results into the GCN.
  • Xiangyu: 1. Construct the few shot tasks: remove some weekends data to test the Meta-MLP and Pretraining. 2. Add the baseline that uses the traditional time-series prediction method. 3. Add more complex backbone models like GCN

2022/03/02

• Attendee: Zhiyuan and Yuanbo, Xiangyu, Yang
• Meeting Summary:
  • Discuss NYC taxi dataset(https://data.cityofnewyork.us/Transportation/NYC-Taxi-Zones/d3c5-ddgc): discuss the details about the datasets, pay attention to whether the pattern of ride-hailing data and taxi data is different.
  • Discuss the training of the base model. How to construct the graph construction of GCN and how to select the centroid? Is given by dataset. The training of GCN is no convergence.
  • Discuss the MLP+META compare with the pretraining method, try more metrics(MAE e.t.) and the task division method
  • Bayesian Graph Model: Read the paper “A Variational Bayesian Approach for Fast Adaptive Air Pollution Prediction”https://ieeexplore.ieee.org/document/9672075 Meta-learning under a Bayesian framework.PPT:https://cloud.tsinghua.edu.cn/f/0b7a4786c75c48108f72/

• TODO:
  • Drew the GCN network structure and have a discussion later.
  • Xiangyu:
    • Deduce how to variation in the bayesian meta-learning.
    • Try more methods in the task division methods in META-MLP.

2022/01/05

• Attendee: Yuanbo, Yang
• Meeting Summary:
  • Yuanbo reading: “MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction”https://arxiv.org/abs/1910.05449v1
  • Discussion about how to produce pathlet by NN or numerical method.https://ieeexplore.ieee.org/document/7293682
  • Discussion about how to predict trajectory using pathlet.

• TODO:
  • Yuanbo: deeper understanding of paper: figure out how the authors predict the trajectory and how they construct the Input representation.
  • Do some research on how to produce pathlet by NN and predict trajectory using pathlet.

2022/01/12

• Attendee: Yuanbo, Yang, Zhiyuan
• Meeting Summary:
  • Discussion about how to produce pathlet by NN using differentiable loss function.
  • Yuanbo propose one method which matches content and index between trajectories and candidate pathlets using slide windows.
  • The detailed info can be referred to this file: zhiyuan_2022_1_12

• TODO:
  • Yuanbo & Zhiyuan: Reimplement the proposed algorithm and do someexperiments on synthetic dataset, like fixed-pattern strings.