{"id":17212001,"url":"https://github.com/refraction-ray/rbm-mcmc","last_synced_at":"2025-09-01T19:34:47.429Z","repository":{"id":101618034,"uuid":"130783044","full_name":"refraction-ray/RBM-MCMC","owner":"refraction-ray","description":"The project explores restricted Boltzmann machine and its potential role in statistical mechanics","archived":false,"fork":false,"pushed_at":"2018-04-30T14:04:10.000Z","size":177,"stargazers_count":0,"open_issues_count":0,"forks_count":2,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-01-30T11:29:48.865Z","etag":null,"topics":["machine-learning","monte-carlo","physics","rbm"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","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/refraction-ray.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":"2018-04-24T02:13:29.000Z","updated_at":"2018-09-07T08:55:40.000Z","dependencies_parsed_at":"2023-06-19T08:46:13.497Z","dependency_job_id":null,"html_url":"https://github.com/refraction-ray/RBM-MCMC","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/refraction-ray%2FRBM-MCMC","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/refraction-ray%2FRBM-MCMC/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/refraction-ray%2FRBM-MCMC/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/refraction-ray%2FRBM-MCMC/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/refraction-ray","download_url":"https://codeload.github.com/refraction-ray/RBM-MCMC/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":245466387,"owners_count":20620193,"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":["machine-learning","monte-carlo","physics","rbm"],"created_at":"2024-10-15T02:59:01.517Z","updated_at":"2025-03-25T12:40:55.401Z","avatar_url":"https://github.com/refraction-ray.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# RBM-MCMC\n\n## Introduction\n\nThis project is designed for an integrated study on the intersection between condensed matter physics and machine learning. Specifically, we pay attention to energy based models (RBM for example) and how can such models play vital roles in statistical mechanics (ensemble probability distribution for example).\n\nWe need two types of tools, **classical Markov chain Monte Carlo** method and **restricted Boltzmann machines** to make our ideas into practice.\n\n## Usage\n\nSee `demo.ipynb` for a general idea on usage. And use `help()` in python anytime you need help.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frefraction-ray%2Frbm-mcmc","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frefraction-ray%2Frbm-mcmc","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frefraction-ray%2Frbm-mcmc/lists"}