{"id":15432851,"url":"https://github.com/mateusroder/e_dropout_rbm","last_synced_at":"2025-03-28T06:11:10.691Z","repository":{"id":98632734,"uuid":"269159217","full_name":"MateusRoder/e_dropout_rbm","owner":"MateusRoder","description":"📄 Official implementation regarding the paper \"Energy-based Dropout in Restricted Boltzmann Machines: Why not go random\".","archived":false,"fork":false,"pushed_at":"2021-04-28T13:44:33.000Z","size":54,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2024-10-18T07:40:18.515Z","etag":null,"topics":["dropout-rbm","energy-dropout","implementation","paper","rbm"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/MateusRoder.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,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2020-06-03T18:03:11.000Z","updated_at":"2024-07-19T17:27:18.000Z","dependencies_parsed_at":null,"dependency_job_id":"285f8465-d603-42d2-95b0-e658ef77e1e2","html_url":"https://github.com/MateusRoder/e_dropout_rbm","commit_stats":{"total_commits":31,"total_committers":2,"mean_commits":15.5,"dds":"0.22580645161290325","last_synced_commit":"a99c696a4aa961a687310f5187a934970ebb4c4c"},"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MateusRoder%2Fe_dropout_rbm","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MateusRoder%2Fe_dropout_rbm/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MateusRoder%2Fe_dropout_rbm/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MateusRoder%2Fe_dropout_rbm/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/MateusRoder","download_url":"https://codeload.github.com/MateusRoder/e_dropout_rbm/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":245978274,"owners_count":20703677,"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":["dropout-rbm","energy-dropout","implementation","paper","rbm"],"created_at":"2024-10-01T18:28:51.258Z","updated_at":"2025-03-28T06:11:10.669Z","avatar_url":"https://github.com/MateusRoder.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Energy-based Dropout in Restricted Boltzmann Machines: Why not go random\n\n*This repository holds all the necessary code to run the very-same experiments described in the paper \"Energy-based Dropout in Restricted Boltzmann Machines: Why not go random\".*\n\n---\n\n## References\n\nIf you use our work to fulfill any of your needs, please cite us:\n\n```BibTex\n@article{roder2020edrop,\n  author={M. {Roder} and G. H. {de Rosa} and V. H. C. {de Albuquerque} and A. L. D. {Rossi} and J. P. {Papa}},\n  journal={IEEE Transactions on Emerging Topics in Computational Intelligence}, \n  title={Energy-Based Dropout in Restricted Boltzmann Machines: Why Not Go Random}, \n  year={2020},\n  volume={},\n  number={},\n  pages={1-11},\n  doi={10.1109/TETCI.2020.3043764}}\n}\n```\n\n---\n\n## Structure\n\n * `utils`\n   * `loader.py`: Utility to load datasets and split them into training, validation and testing sets;\n   * `objects.py`: Wraps objects instantiation for command line usage;\n   \n   \n---\n\n## Package Guidelines\n\n### Installation\n\nInstall all the pre-needed requirements using:\n\n```Python\npip install -r requirements.txt\n```\n*If you encounter any problems with the automatic installation of the [learnergy](https://github.com/gugarosa/learnergy) package, contact us.*\n\n### Data configuration\n\nIn order to run the experiments, you can use `torchvision` to load pre-implemented datasets.\n\n---\n\n## Usage\n\n### Model Training and Reconstruction\n\nThe experiment is conducted by pre-training an RBM architecture and post-evaluating them. To accomplish such a step, one needs to use the following script:\n\n```Python\npython rbm_reconstruction.py -h\n```\n\n*Note that `-h` invokes the script helper, which assists users in employing the appropriate parameters.*\n\n### Bash Script\n\nInstead of invoking every script to conduct the experiments, it is also possible to use the provided shell script, as follows:\n\n```Bash\n./pipeline.sh\n```\n\nSuch a script will conduct every step needed to accomplish the experimentation used throughout this paper. Furthermore, one can change any input argument that is defined in the script.\n\n---\n\n## Support\n\nWe know that we do our best, but it is inevitable to acknowledge that we make mistakes. If you ever need to report a bug, report a problem, talk to us, please do so! We will be available at our bests at this repository or mateus.roder@unesp.br and gustavo.rosa@unesp.br.\n\n---\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmateusroder%2Fe_dropout_rbm","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmateusroder%2Fe_dropout_rbm","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmateusroder%2Fe_dropout_rbm/lists"}