Probabilistic Parametric Curves for Sequence Modeling
This work proposes a probabilistic extension to Bézier curves as a basis for effectively modeling stochastic processes with a bounded index set. The proposed stochastic process model is based on Mixture Density Networks and Bézier curves with Gaussian random variables as control points. A key advant...
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| Автор: | |
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| Формат: | Online |
| Мова: | Англійська |
| Опубліковано: |
KIT Scientific Publishing
2022
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| Предмети: | |
| Онлайн доступ: | ONIX_20220718_9783731511984_116 |
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| _version_ | 1863736317323509760 |
|---|---|
| author | Hug, Ronny |
| author_browse | Hug, Ronny |
| author_facet | Hug, Ronny |
| author_sort | Hug, Ronny |
| collection | Directory of Open Access Books |
| description | This work proposes a probabilistic extension to Bézier curves as a basis for effectively modeling stochastic processes with a bounded index set. The proposed stochastic process model is based on Mixture Density Networks and Bézier curves with Gaussian random variables as control points. A key advantage of this model is given by the ability to generate multi-mode predictions in a single inference step, thus avoiding the need for Monte Carlo simulation. |
| format | Online |
| id | doab-20.500.12854ir-90637 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | KIT Scientific Publishing |
| publisherStr | KIT Scientific Publishing |
| record_format | ojs |
| spelling | doab-20.500.12854ir-906372025-07-30T11:55:53Z Probabilistic Parametric Curves for Sequence Modeling Hug, Ronny Probabilistische Sequenzmodellierung Stochastische Prozesse Neuronale Netzwerke Parametrische Kurven Probabilistic Sequence Modeling Stochastic Processes Neural Networks Parametric Curves thema EDItEUR::U Computing and Information Technology::UY Computer science::UYA Mathematical theory of computation::UYAM Maths for computer scientists thema EDItEUR::U Computing and Information Technology::UY Computer science::UYA Mathematical theory of computation::UYAM Maths for computer scientists This work proposes a probabilistic extension to Bézier curves as a basis for effectively modeling stochastic processes with a bounded index set. The proposed stochastic process model is based on Mixture Density Networks and Bézier curves with Gaussian random variables as control points. A key advantage of this model is given by the ability to generate multi-mode predictions in a single inference step, thus avoiding the need for Monte Carlo simulation. 2022-08-03T05:36:02Z 2022-08-03T05:36:02Z 2022-07-18T11:55:27Z 2022 book ONIX_20220718_9783731511984_116 OCN: 1348377297 1863-6489 https://library.oapen.org/handle/20.500.12657/57539 9783731511984 https://directory.doabooks.org/handle/20.500.12854/90637 eng Karlsruher Schriften zur Anthropomatik open access image/jpeg image/jpeg image/jpeg image/jpeg n/a n/a n/a n/a https://library.oapen.org/bitstream/20.500.12657/57539/1/9783731511984.pdf https://library.oapen.org/bitstream/20.500.12657/57539/1/9783731511984.pdf https://library.oapen.org/bitstream/20.500.12657/57539/1/9783731511984.pdf https://library.oapen.org/bitstream/20.500.12657/57539/1/9783731511984.pdf KIT Scientific Publishing KIT Scientific Publishing 10.5445/KSP/1000146434 10.5445/KSP/1000146434 68fffc18-8f7b-44fa-ac7e-0b7d7d979bd2 9783731511984 AG Universitätsverlage KIT Scientific Publishing 226 Karlsruhe open access |
| spellingShingle | Probabilistische Sequenzmodellierung Stochastische Prozesse Neuronale Netzwerke Parametrische Kurven Probabilistic Sequence Modeling Stochastic Processes Neural Networks Parametric Curves thema EDItEUR::U Computing and Information Technology::UY Computer science::UYA Mathematical theory of computation::UYAM Maths for computer scientists thema EDItEUR::U Computing and Information Technology::UY Computer science::UYA Mathematical theory of computation::UYAM Maths for computer scientists Hug, Ronny Probabilistic Parametric Curves for Sequence Modeling |
| title | Probabilistic Parametric Curves for Sequence Modeling |
| title_full | Probabilistic Parametric Curves for Sequence Modeling |
| title_fullStr | Probabilistic Parametric Curves for Sequence Modeling |
| title_full_unstemmed | Probabilistic Parametric Curves for Sequence Modeling |
| title_short | Probabilistic Parametric Curves for Sequence Modeling |
| title_sort | probabilistic parametric curves for sequence modeling |
| topic | Probabilistische Sequenzmodellierung Stochastische Prozesse Neuronale Netzwerke Parametrische Kurven Probabilistic Sequence Modeling Stochastic Processes Neural Networks Parametric Curves thema EDItEUR::U Computing and Information Technology::UY Computer science::UYA Mathematical theory of computation::UYAM Maths for computer scientists thema EDItEUR::U Computing and Information Technology::UY Computer science::UYA Mathematical theory of computation::UYAM Maths for computer scientists |
| topic_facet | Probabilistische Sequenzmodellierung Stochastische Prozesse Neuronale Netzwerke Parametrische Kurven Probabilistic Sequence Modeling Stochastic Processes Neural Networks Parametric Curves thema EDItEUR::U Computing and Information Technology::UY Computer science::UYA Mathematical theory of computation::UYAM Maths for computer scientists thema EDItEUR::U Computing and Information Technology::UY Computer science::UYA Mathematical theory of computation::UYAM Maths for computer scientists |
| url | ONIX_20220718_9783731511984_116 |
| work_keys_str_mv | AT hugronny probabilisticparametriccurvesforsequencemodeling |