{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/gradient-estimators-for-implicit-models","title":"Gradient Estimators for Implicit Models","arxiv_id":"1705.07107","date":"2017-05-19","proceeding":"ICLR 2018 1","authors":["Yingzhen Li","Richard E. Turner"],"abstract":"Implicit models, which allow for the generation of samples but not for\npoint-wise evaluation of probabilities, are omnipresent in real-world problems\ntackled by machine learning and a hot topic of current research. Some examples\ninclude data simulators that are widely used in engineering and scientific\nresearch, generative adversarial networks (GANs) for image synthesis, and\nhot-off-the-press approximate inference techniques relying on implicit\ndistributions. The majority of existing approaches to learning implicit models\nrely on approximating the intractable distribution or optimisation objective\nfor gradient-based optimisation, which is liable to produce inaccurate updates\nand thus poor models. This paper alleviates the need for such approximations by\nproposing the Stein gradient estimator, which directly estimates the score\nfunction of the implicitly defined distribution. The efficacy of the proposed\nestimator is empirically demonstrated by examples that include meta-learning\nfor approximate inference, and entropy regularised GANs that provide improved\nsample diversity.","url_abs":"http://arxiv.org/abs/1705.07107v5","url_pdf":"http://arxiv.org/pdf/1705.07107v5.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"gradient-estimators-for-implicit-models","repo_url":"https://github.com/YingzhenLi/SteinGrad","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"diversity","task_name":"Diversity"},{"task_slug":"image-generation","task_name":"Image Generation"},{"task_slug":"meta-learning","task_name":"Meta-Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1705.07107","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}