{"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/the-neural-hawkes-process-a-neurally-self","title":"The Neural Hawkes Process: A Neurally Self-Modulating Multivariate Point Process","arxiv_id":"1612.09328","date":"2016-12-29","proceeding":"NeurIPS 2017 12","authors":["Hongyuan Mei","Jason Eisner"],"abstract":"Many events occur in the world. Some event types are stochastically excited\nor inhibited---in the sense of having their probabilities elevated or\ndecreased---by patterns in the sequence of previous events. Discovering such\npatterns can help us predict which type of event will happen next and when. We\nmodel streams of discrete events in continuous time, by constructing a neurally\nself-modulating multivariate point process in which the intensities of multiple\nevent types evolve according to a novel continuous-time LSTM. This generative\nmodel allows past events to influence the future in complex and realistic ways,\nby conditioning future event intensities on the hidden state of a recurrent\nneural network that has consumed the stream of past events. Our model has\ndesirable qualitative properties. It achieves competitive likelihood and\npredictive accuracy on real and synthetic datasets, including under\nmissing-data conditions.","url_abs":"http://arxiv.org/abs/1612.09328v3","url_pdf":"http://arxiv.org/pdf/1612.09328v3.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":"the-neural-hawkes-process-a-neurally-self","repo_url":"https://github.com/HMEIatJHU/neurawkes","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"the-neural-hawkes-process-a-neurally-self","repo_url":"https://github.com/WangHexie/large_hawkes","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"the-neural-hawkes-process-a-neurally-self","repo_url":"https://github.com/Yoontae6719/Point-Processes","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"the-neural-hawkes-process-a-neurally-self","repo_url":"https://github.com/hongrui24/neuralhawkespytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"the-neural-hawkes-process-a-neurally-self","repo_url":"https://github.com/sohamch/Neural-Hawkes-study","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"the-neural-hawkes-process-a-neurally-self","repo_url":"https://github.com/vladislavzh/cotic","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"the-neural-hawkes-process-a-neurally-self","repo_url":"https://github.com/xiao03/nh","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"the-neural-hawkes-process-a-neurally-self","repo_url":"https://github.com/znhy1024/heard","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"the-neural-hawkes-process-a-neurally-self","repo_url":"https://github.com/ivan-chai/hotpp-benchmark","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[],"methods":[{"method_slug":"lstm","method_name":"LSTM"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"tanh-activation","method_name":"Tanh Activation"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1612.09328","atlas_url":"https://app.syntology.ai/?focus=1612.09328","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}