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Codes for paper "Stochastic Deep Gaussian Processes over Graphs"

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Stochastic Deep Gaussian Processes over Graphs

code and results for a NeurIPS2020 paper submission

Prerequests

our implementation is mainly based on following packages:

python 3.7
pip install keras==2.3.1
pip install gpuinfo
pip install tensorflow-gpu==1.15
pip install gpflow==1.5

Besides, some basic packages like numpy are also needed.

Specification

Source code and experiment result are both provided.

Files

  • src/
    • doubly_stochastic_dgp: codes from repository DGP
    • compatible: codes to make the DGP source codes compatible with gpflow1.5.
    • gpflow_monitor: monitoring tool for gpflow models, from this repo.
    • data: datasets.
    • dgp_graph: implemetation of our model.
    • *.ipynb: jupyter notebooks for experiments.
    • run_toy.sh: shell script to run additional experiment.
    • toy_main.py: code for additional experiment (Traditional ML methods and DGPG with linear kernel).
  • results/: contains results of experiments
    • *.html: experiment results demonstrated by static HTML files.

Experiments

The experiments are demonstrated by jupyter notebooks. The source is under directory src/ and the corresponding result is exported as a static HTML file stored in the directory results/. They are organized by dataset names:

  1. Synthetic Datasets
    • demo_toy_run1.ipynb
    • demo_toy_run2.ipynb
    • demo_toy_run3.ipynb
    • demo_toy_run4.ipynb
    • demo_toy_run5.ipynb
  2. Small Datasets
    • demo_city45.ipynb
    • demo_city45_linear.ipynb (linear kernel)
    • demo_city45_baseline.ipynb (traditional regression methods)
    • demo_etex.ipynb
    • demo_etex_linear.ipynb
    • demo_etex_baseline.ipynb
    • demo_fmri.ipynb
    • demo_fmri_linear.ipynb
    • demo_fmri_baseline.ipynb
  3. Large Datasets (traffic flow)
    • LA
      • demo_la_15min.ipynb
      • demo_la_30min.ipynb
      • demo_la_60min.ipynb
    • BAY
      • demo_bay_15min.ipynb
      • demo_bay_30min.ipynb
      • demo_bay_60min.ipynb

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Codes for paper "Stochastic Deep Gaussian Processes over Graphs"

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