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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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Đã lưu trong:
Tác giả chính: Hug, Ronny
Định dạng: Online
Ngôn ngữ:Tiếng Anh
Được phát hành: KIT Scientific Publishing 2022
Những chủ đề:
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
Truy cập trực tuyến:ONIX_20220718_9783731511984_116
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Tóm tắt: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.

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