{"id":19603905,"url":"https://github.com/divelab/rmwggis","last_synced_at":"2025-04-27T19:32:28.855Z","repository":{"id":107289905,"uuid":"548620182","full_name":"divelab/RMwGGIS","owner":"divelab","description":"Official implementation of \"Gradient-Guided Importance Sampling for Learning Binary Energy-Based Models\" [ICLR2023]","archived":false,"fork":false,"pushed_at":"2023-01-31T20:40:48.000Z","size":3013,"stargazers_count":9,"open_issues_count":1,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-04-05T02:21:43.253Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/divelab.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"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":"2022-10-09T23:26:08.000Z","updated_at":"2024-05-22T20:09:49.000Z","dependencies_parsed_at":null,"dependency_job_id":"d65e43fd-98db-47a5-b124-6aaad028594d","html_url":"https://github.com/divelab/RMwGGIS","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/divelab%2FRMwGGIS","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/divelab%2FRMwGGIS/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/divelab%2FRMwGGIS/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/divelab%2FRMwGGIS/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/divelab","download_url":"https://codeload.github.com/divelab/RMwGGIS/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":251195920,"owners_count":21550870,"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":[],"created_at":"2024-11-11T09:33:37.918Z","updated_at":"2025-04-27T19:32:28.849Z","avatar_url":"https://github.com/divelab.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Gradient-Guided Importance Sampling for Learning Binary Energy-Based Models\n\nThis is the official implementation of the **RMwGGIS** method proposed in the following paper.\n\nMeng Liu, Haoran Liu, and Shuiwang Ji. \"[Gradient-Guided Importance Sampling for Learning Binary Energy-Based Models](https://openreview.net/forum?id=9DZKk85Z4zA)\". [ICLR 2023]\n\n\u003cp align=\"center\"\u003e\n\u003cimg src=\"https://github.com/divelab/RMwGGIS/blob/main/assets/RMwGGIS.png\" width=\"600\" class=\"center\" alt=\"\"/\u003e\n    \u003cbr/\u003e\n\u003c/p\u003e\n\u003cp align = \"center\"\u003e\nVisualization of learned energy functions on 32-dimensional synthetic discrete datasets.\n\u003c/p\u003e\n\nThere is [an implementation from the community](https://github.com/J-zin/RMwGGIS) as well.\n\n## Requirements\nWe include key dependencies below.\n* PyTorch\n* tqdm\n* sympy\n* distutils\n\n## Run\nTo run the experiments on synthetic discrete data, please refer to the commands in [`run.sh`](https://github.com/divelab/RMwGGIS/blob/main/RMwGGIS/run.sh).\n\n## Reference\n```\n@inproceedings{liu2023rmwggis,\n  title={Gradient-Guided Importance Sampling for Learning Binary Energy-Based Models},\n  author={Liu, Meng and Liu, Haoran and Ji, Shuiwang},\n  booktitle={International Conference on Learning Representations},\n  year={2023}\n}\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdivelab%2Frmwggis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdivelab%2Frmwggis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdivelab%2Frmwggis/lists"}