{"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/wasserstein-learning-of-deep-generative-point","title":"Wasserstein Learning of Deep Generative Point Process Models","arxiv_id":"1705.08051","date":"2017-05-23","proceeding":"NeurIPS 2017 12","authors":["Shuai Xiao","Mehrdad Farajtabar","Xiaojing Ye","Junchi Yan","Le Song","Hongyuan Zha"],"abstract":"Point processes are becoming very popular in modeling asynchronous sequential\ndata due to their sound mathematical foundation and strength in modeling a\nvariety of real-world phenomena. Currently, they are often characterized via\nintensity function which limits model's expressiveness due to unrealistic\nassumptions on its parametric form used in practice. Furthermore, they are\nlearned via maximum likelihood approach which is prone to failure in\nmulti-modal distributions of sequences. In this paper, we propose an\nintensity-free approach for point processes modeling that transforms nuisance\nprocesses to a target one. Furthermore, we train the model using a\nlikelihood-free leveraging Wasserstein distance between point processes.\nExperiments on various synthetic and real-world data substantiate the\nsuperiority of the proposed point process model over conventional ones.","url_abs":"http://arxiv.org/abs/1705.08051v1","url_pdf":"http://arxiv.org/pdf/1705.08051v1.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":"wasserstein-learning-of-deep-generative-point","repo_url":"https://github.com/xiaoshuai09/Wasserstein-Learning-For-Point-Process","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"point-processes","task_name":"Point Processes"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1705.08051","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}