K
Klaus Diepold
Researcher at Technische Universität München
Publications - 269
Citations - 3465
Klaus Diepold is an academic researcher from Technische Universität München. The author has contributed to research in topics: Video quality & Computer science. The author has an hindex of 24, co-authored 248 publications receiving 2962 citations. Previous affiliations of Klaus Diepold include Information Technology Institute & Ludwig Maximilian University of Munich.
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Journal ArticleDOI
Best Practices for QoE Crowdtesting: QoE Assessment With Crowdsourcing
Tobias Hossfeld,Christian Keimel,Matthias Hirth,Bruno Gardlo,Julian Habigt,Klaus Diepold,Phuoc Tran-Gia +6 more
TL;DR: The focus of this article is on the issue of reliability and the use of video quality assessment as an example for the proposed best practices, showing that the recommended two-stage QoE crowdtesting design leads to more reliable results.
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First steps towards an intelligent laser welding architecture using deep neural networks and reinforcement learning
TL;DR: The intelligent laser-welding architecture introduced in this work has the capacity to improve its performance without further human assistance and therefore addresses key requirements of modern industry.
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Multi-agent deep reinforcement learning: a survey
Sven Gronauer,Klaus Diepold +1 more
TL;DR: This article provides an overview of the current developments in the field of multi-agent deep reinforcement learning, focusing primarily on literature from recent years that combinesDeep reinforcement learning methods with a multi- agent scenario.
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Analysis Operator Learning and its Application to Image Reconstruction
TL;DR: This paper presents an algorithm for learning an analysis operator from training images based on lp-norm minimization on the set of full rank matrices with normalized columns, and carefully introduces the employed conjugate gradient method on manifolds.
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Intelligent laser welding through representation, prediction, and control learning: An architecture with deep neural networks and reinforcement learning
TL;DR: The proposed intelligent laser-welding architecture combines representation, prediction, and control learning: three of the main hallmarks of an intelligent system and promises to address several key requirements of modern industry.