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2020 AAAI AAAI 2020

Introducing Probabilistic Bézier Curves for N-Step Sequence Prediction

Abstract

Abstract Representations of sequential data are commonly based on the assumption that observed sequences are realizations of an unknown underlying stochastic process, where the learning problem includes determination of the model parameters. In this context, a model must be able to capture the multi-modal nature of the data, without blurring between single modes. This paper proposes probabilistic B'{e}zier curves (

🌉 Interdisciplinary Bridge — Data Science & Analytics and Machine Learning
🧭 Keyword Pioneer — bezier curve
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

Ronny Hug , Wolfgang Hübner , Michael Arens

Topics

Machine Learning > Core Methods > Representation Learning Data Science & Analytics > Methods > Time Series Data Science & Analytics > Methods > Time Series Analysis Machine Learning > Learning Types > Generative Models

Keywords

probabilistic modeling stochastic process sequence prediction probabilistic model sequential datum bezier curve multi-modal datum multi-modal prediction
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