Learning in the Machine: To Share or not to Share?

This repository is associated with our paper.

@article{ott2020learning,
  title={Learning in the machine: To share or not to share?},
  author={Ott, Jordan and Linstead, Erik and LaHaye, Nicholas and Baldi, Pierre},
  journal={Neural Networks},
  year={2020},
  publisher={Elsevier}
}

Pipeline

1. Hyperparameter grid search

• Number of layers
• Learning rate

2. Full experiments

• Types of augmentation
  • Edge noise
  • Translation
  • Rotation
  • Noise
  • Quadrant swap

3. Results

• Metrics
  • Loss
  • Validation loss
  • Training accuracy
  • Validation accuracy
  • Edge noise augmented validation accuracy
  • Translation augmented validation accuracy
  • Noise augmented validation accuracy
  • Rotation augmented validation accuracy
• Approximate weight sharing
  • Observing the distance between free convolutional filters within a layer
• Variable Connection Patterns
  • Varying the probability