Probabilistic Parametric Curves for Sequence Modeling PDF Download
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Author: Hug, Ronny
Publisher: KIT Scientific Publishing
ISBN: 3731511983
Category : Mathematics
Languages : en
Pages : 224
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Book 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.
Author: Hug, Ronny
Publisher: KIT Scientific Publishing
ISBN: 3731511983
Category : Mathematics
Languages : en
Pages : 224
Get Book Here
Book 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.
Author: Dürr, Fabian
Publisher: KIT Scientific Publishing
ISBN: 3731513145
Category :
Languages : en
Pages : 248
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Book Description
The understanding and interpretation of complex 3D environments is a key challenge of autonomous driving. Lidar sensors and their recorded point clouds are particularly interesting for this challenge since they provide accurate 3D information about the environment. This work presents a multimodal approach based on deep learning for panoptic segmentation of 3D point clouds. It builds upon and combines the three key aspects multi view architecture, temporal feature fusion, and deep sensor fusion.
Author: Beyerer, Jürgen
Publisher: KIT Scientific Publishing
ISBN: 3731513048
Category :
Languages : en
Pages : 140
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Book Description
In August 2022, Fraunhofer IOSB and IES of KIT held a joint workshop in a Schwarzwaldhaus near Triberg. Doctoral students presented research reports and discussed various topics like computer vision, optical metrology, network security, usage control, and machine learning. This book compiles the workshop's results and ideas, offering a comprehensive overview of the research program of IES and Fraunhofer IOSB.
Author: Kalb, Tobias Michael
Publisher: KIT Scientific Publishing
ISBN: 3731513730
Category :
Languages : en
Pages : 236
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Book Description
Deep learning excels at extracting complex patterns but faces catastrophic forgetting when fine-tuned on new data. This book investigates how class- and domain-incremental learning affect neural networks for automated driving, identifying semantic shifts and feature changes as key factors. Tools for quantitatively measuring forgetting are selected and used to show how strategies like image augmentation, pretraining, and architectural adaptations mitigate catastrophic forgetting.
Author: Péter Galambos
Publisher: Springer Nature
ISBN: 3031196503
Category : Computers
Languages : en
Pages : 241
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Book Description
This volume constitutes the papers of two workshops which were held in conjunctionwith the First International Conference on Robotics, Computer Vision and Intelligent Systems,ROBOVIS 2020, Virtual Event, in November 4-6, 2020 and Second International Conference on Robotics, Computer Vision and Intelligent Systems,ROBOVIS 2021, Virtual Event, in October 25-27, 2021. The 11 revised full papers presented in this book were carefully reviewed and selectedfrom 53 submissions.
Author: Pfrommer, Julius
Publisher: KIT Scientific Publishing
ISBN: 373151253X
Category :
Languages : en
Pages : 210
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Book Description
In dieser Arbeit wird ein Ansatz entwickelt, um eine automatische Anpassung des Verhaltens von Produktionsanlagen an wechselnde Aufträge und Rahmenbedingungen zu erreichen. Dabei kommt das Prinzip der Selbstorganisation durch verteilte Planung zum Einsatz. - Most production processes are rigid not only by way of the physical layout of machines and their integration, but also by the custom programming of the control logic for the integration of components to a production systems. Changes are time- and resource-expensive. This makes the production of small lot sizes of customized products economically challenging. This work develops solutions for the automated adaptation of production systems based on self-organisation and distributed planning.
Author: Meshram, Ankush
Publisher: KIT Scientific Publishing
ISBN: 3731512572
Category :
Languages : en
Pages : 224
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Book Description
Configuring an anomaly-based Network Intrusion Detection System for cybersecurity of an industrial system in the absence of information on networking infrastructure and programmed deterministic industrial process is challenging. Within the research work, different self-learning frameworks to analyze passively captured network traces from PROFINET-based industrial system for protocol-based and process behavior-based anomaly detection are developed, and evaluated on a real-world industrial system.
Author: Birte U. Forstmann
Publisher: Springer Nature
ISBN: 3031452712
Category :
Languages : en
Pages : 384
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Book Description
Author: Douglas C. Montgomery
Publisher: John Wiley & Sons
ISBN: 1119570611
Category :
Languages : en
Pages : 722
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Book Description
Author: Tore Schweder
Publisher: Cambridge University Press
ISBN: 1316445054
Category : Mathematics
Languages : en
Pages : 521
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Book Description
This lively book lays out a methodology of confidence distributions and puts them through their paces. Among other merits, they lead to optimal combinations of confidence from different sources of information, and they can make complex models amenable to objective and indeed prior-free analysis for less subjectively inclined statisticians. The generous mixture of theory, illustrations, applications and exercises is suitable for statisticians at all levels of experience, as well as for data-oriented scientists. Some confidence distributions are less dispersed than their competitors. This concept leads to a theory of risk functions and comparisons for distributions of confidence. Neyman–Pearson type theorems leading to optimal confidence are developed and richly illustrated. Exact and optimal confidence distribution is the gold standard for inferred epistemic distributions. Confidence distributions and likelihood functions are intertwined, allowing prior distributions to be made part of the likelihood. Meta-analysis in likelihood terms is developed and taken beyond traditional methods, suiting it in particular to combining information across diverse data sources.
