https://github.com/uber-research/differentiable-plasticity

Implementations of the algorithms described in Differentiable plasticity: training plastic networks with gradient descent, a research paper from Uber AI Labs.
https://github.com/uber-research/differentiable-plasticity

ai differentiable-plasticity machine-learning machine-learning-algorithms ml

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Implementations of the algorithms described in Differentiable plasticity: training plastic networks with gradient descent, a research paper from Uber AI Labs.

• Host: GitHub
• URL: https://github.com/uber-research/differentiable-plasticity
• Owner: uber-research
• License: other
• Created: 2018-03-28T15:42:06.000Z (almost 7 years ago)
• Default Branch: master
• Last Pushed: 2019-10-23T17:26:39.000Z (about 5 years ago)
• Last Synced: 2025-01-07T10:09:46.733Z (13 days ago)
• Topics: ai, differentiable-plasticity, machine-learning, machine-learning-algorithms, ml
• Language: Python
• Homepage:
• Size: 8.21 MB
• Stars: 399
• Watchers: 27
• Forks: 70
• Open Issues: 3
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
## Differentiable plasticity

This repo contains implementations of the algorithms described in [Differentiable plasticity: training plastic networks with gradient descent](https://arxiv.org/abs/1804.02464), a research paper from Uber AI Labs.

NOTE: please see also our more recent work on differentiable *neuromodulated* plasticity: the "[backpropamine](https://github.com/uber-research/backpropamine)" framework.

There are four different experiments included here:

- `simple`: Binary pattern memorization and completion. Read this one first!

- `images`: Natural image memorization and completion

- `omniglot`: One-shot learning in the Omniglot task

- `maze`: Maze exploration task (reinforcement learning)

We strongly recommend studying the `simple/simplest.py` program first, as it is deliberately kept as simple as possible while showing full-fledged differentiable plasticity learning.

The code requires Python 3 and PyTorch 0.3.0 or later. The `images` code also requires scikit-learn. By default our code requires a GPU, but most programs can be run on CPU by simply uncommenting the relevant lines (for others, remove all occurrences of `.cuda()`).

To comment, please open an issue. We will not be accepting pull requests but encourage further study of this research. To learn more, check out our accompanying article on the [Uber Engineering Blog](https://eng.uber.com/differentiable-plasticity).

## Copyright and licensing information

Copyright (c) 2018-2019 Uber Technologies, Inc.

All code is licensed under the Uber Non-Commercial License (the "License");

you may not use this file except in compliance with the License.

You may obtain a copy of the License at the root directory of this project. 

See the LICENSE file in this repository for the specific language governing 

permissions and limitations under the License.