{"id":21630976,"url":"https://github.com/uber-research/differentiable-plasticity","last_synced_at":"2025-04-06T21:15:13.374Z","repository":{"id":41113890,"uuid":"127162796","full_name":"uber-research/differentiable-plasticity","owner":"uber-research","description":"Implementations of the algorithms described in Differentiable plasticity: training plastic networks with gradient descent, a research paper from Uber AI Labs.","archived":false,"fork":false,"pushed_at":"2019-10-23T17:26:39.000Z","size":8606,"stargazers_count":401,"open_issues_count":3,"forks_count":71,"subscribers_count":26,"default_branch":"master","last_synced_at":"2025-03-30T19:09:05.583Z","etag":null,"topics":["ai","differentiable-plasticity","machine-learning","machine-learning-algorithms","ml"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/uber-research.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2018-03-28T15:42:06.000Z","updated_at":"2025-02-16T02:17:52.000Z","dependencies_parsed_at":"2022-09-09T21:31:22.160Z","dependency_job_id":null,"html_url":"https://github.com/uber-research/differentiable-plasticity","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/uber-research%2Fdifferentiable-plasticity","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/uber-research%2Fdifferentiable-plasticity/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/uber-research%2Fdifferentiable-plasticity/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/uber-research%2Fdifferentiable-plasticity/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/uber-research","download_url":"https://codeload.github.com/uber-research/differentiable-plasticity/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247550690,"owners_count":20956987,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["ai","differentiable-plasticity","machine-learning","machine-learning-algorithms","ml"],"created_at":"2024-11-25T02:12:52.448Z","updated_at":"2025-04-06T21:15:13.350Z","avatar_url":"https://github.com/uber-research.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"## Differentiable plasticity\n\nThis 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.\n\nNOTE: please see also our more recent work on differentiable *neuromodulated* plasticity: the \"[backpropamine](https://github.com/uber-research/backpropamine)\" framework.\n\nThere are four different experiments included here:\n\n- `simple`: Binary pattern memorization and completion. Read this one first!\n- `images`: Natural image memorization and completion\n- `omniglot`: One-shot learning in the Omniglot task\n- `maze`: Maze exploration task (reinforcement learning)\n\n\nWe 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.\n\nThe 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()`).\n\nTo 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).\n\n## Copyright and licensing information\n\nCopyright (c) 2018-2019 Uber Technologies, Inc.\n\nAll code is licensed under the Uber Non-Commercial License (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at the root directory of this project. \n\nSee the LICENSE file in this repository for the specific language governing \npermissions and limitations under the License. \n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fuber-research%2Fdifferentiable-plasticity","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fuber-research%2Fdifferentiable-plasticity","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fuber-research%2Fdifferentiable-plasticity/lists"}