Skip to content

Decomposing End-to-end Backpropagation based on Auto-encoders and Target Propagation

Notifications You must be signed in to change notification settings

4hfly/Associated_Learning

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

65 Commits
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Associated_Learning

Requirements

pip install -r requirements.txt

Datasets

  • For AGNews and DBpedia, dataset will be automatically downloaded during the training.
  • For SST-2, please download the dataset from GLUE Benchmark and put the files into ./data/sst2/.

Execution

We use json file for the configuration. Before running the code, please check hyperparameters.json and select proper parameters.

Then just simply run:

python -m associated_learning.main

Citation

About

Decomposing End-to-end Backpropagation based on Auto-encoders and Target Propagation

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Python 100.0%