https://github.com/unixpickle/sgdstore

Augmented RNN memory via live SGD
https://github.com/unixpickle/sgdstore

Last synced: about 2 months ago
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Augmented RNN memory via live SGD

• Host: GitHub
• URL: https://github.com/unixpickle/sgdstore
• Owner: unixpickle
• Created: 2017-03-24T19:37:21.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2017-04-25T23:06:56.000Z (over 7 years ago)
• Last Synced: 2024-06-08T01:34:46.043Z (4 months ago)
• Language: Go
• Size: 5.59 MB
• Stars: 32
• Watchers: 4
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# sgdstore

This is a memory-augmented neural network that uses a neural network as a storage device. Particularly, a *controller network* provides training examples for a *storage network* at every timestep. The *storage network* is trained on these samples with SGD at every timestep (in a differentiable manner). The controller can then query the storage network by feeding it inputs and seeing the corresponding outputs. The end result is that the storage network serves as a memory bank which is "written to" via SGD.

# Hypotheses

Neural networks seem to provide a lot of desirable properties as memory modules:

 * They can store a lot of information.

 * They can compress information.

 * They can generalize to new information.

 * They can interpolate between training samples.

In a sense, a neural network can be seen as a key-value store which tries to generalize to new keys. This seems like the perfect memory structure for a memory-augmented neural network.

# Results

Preliminary results on polynomial approximation looked promising. See [experiments/poly_approx](experiments/poly_approx). After seeing those results, I decided to scale up to a harder meta-learning task.

On the Omniglot handwriting dataset, the model (controlled by a vanilla RNN) outperformed an LSTM in training time (measured in epochs) and accuracy. See [experiments/omniglot](experiments/omniglot).