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Publications

Displaying results 311 to 320 of 666.

  1. Jens Popper; Martin Ruskowski

    Using Multi-Agent Deep Reinforcement Learning For Flexible Job Shop Scheduling Problems

    In: Roberto Teti; Doriana M. D'Addona (Hrsg.). CIRP Proceedings. CIRP Conference on Intelligent Computation in Manufacturing Engineering (CIRP ICME), Pages 63-67, Vol. 112, Special Issue 15th CIRP Conference on Intelligent Computation in Manufacturing Engineering, 14-16 July 2019, Gulf of Naples, Italy, Elsevier B.V. 2021.

  2. Jens Popper; Vassilios Yfantis; Martin Ruskowski (Hrsg.)

    Simultaneous Production and AGV Scheduling using Multi-Agent Deep Reinforcement Learning

    CIRP Conference on Manufactoring Systems (CIRP CMS-2021), 54th CIRP Conference on Manufacturing Systems, 2021, located at CIRP, September 22-24, Athens, Greece, ELSEVIER, 2021.

  3. Jens Popper; William Motsch; Alexander David; Teresa Petzsche; Martin Ruskowski (Hrsg.)

    Utilizing Multi-Agent Deep Reinforcement Learning For Flexible Job Shop Scheduling Under Sustainable Viewpoints

    International Conference on Electrical, Computer, Communications and Mechatronics Engineering, located at 2021 International Conference on Electrical, Computer, Communications and Mechatronics Engineering, October 7-8, Belle Mare, Mauritius, IEEE, 2021.

  4. Kumail Raza; René Schuster; Didier Stricker

    Multi-scale Iterative Residuals for Fast and Scalable Stereo Matching

    In: Computer Science in Cars Symposium. ACM Computer Science in Cars Symposium (CSCS-2021), November 30, Ingolstadt, Germany, ACM, 2021.

  5. Hendrik Wöhrle; Mariela De Lucas Alvarez; Fabian Schlenke; Alexander Walsemann; Michael Karagounis; Frank Kirchner (Hrsg.)

    Surrogate Model based Co-Optimization of Deep Neural Network Hardware Accelerators

    IEEE International Midwest Symposium on Circuits and Systems (MWSCAS-2021), August 9-11, USA, IEEE, 8/2021.

  6. Adriano Lucieri; Muhammad Naseer Bajwa; Andreas Dengel; Sheraz Ahmed

    Achievements and Challenges in Explaining Deep Learning based Computer-Aided Diagnosis Systems

    In: ArXiv e-prints (arxiv), Vol. 1, Pages 1-17, 11/2020.

  7. Roberto V. Zicari; Sheraz Ahmed; Julia Amann; Stephan Alexander Braun; John Brodersen; Frédérick Bruneault; James Brusseau; Erik Campano; Megan Coffee; Andreas Dengel; Boris Düdder; Alessio Gallucci; Thomas Krendl Gilbert; Philippe Gottfrois; Emmanuel Goffi; Christoffer Bjerre Haase; Thilo Hagendorff; Eleanore Hickman; Elisabeth Hildt; Sune Holm; Pedro Kringen; Ulrich Kühne; Adriano Lucieri; Vince I. Madai; Pedro A. Moreno-Sánchez; Oriana Medlicott; Matiss Ozols; Eberhard Schnebel; Andy Spezzatti; Jesmin Jahan Tithi; Steven Umbrello; Dennis Vetter; Holger Volland; Magnus Westerlund; Renee Wurth

    Co-Design of a Trustworthy AI System in Healthcare: Deep Learning Based Skin Lesion Classifier

    In: Remo Pareschi (Hrsg.). Frontiers in Human Dynamics, Vol. 3, Pages 1-40, Frontiers Media S.A. Lausanne, 7/2021.

  8. Adriano Lucieri; Andreas Dengel; Sheraz Ahmed

    Deep Learning Based Decision Support for Medicine -- A Case Study on Skin Cancer Diagnosis

    In: ArXiv e-prints (arxiv), Vol. 1, Pages 1-29, arXiv, 3/2021.

  9. Dominique Mercier; Adriano Lucieri; Mohsin Munir; Andreas Dengel; Sheraz Ahmed

    Evaluating Privacy-Preserving Machine Learning in Critical Infrastructures: A Case Study on Time-Series Classification

    In: IEEE Transactions on Industrial Informatics, Vol. 18, No. 11, Pages 7834-7842, IEEE, 11/2021.

  10. Vitor Fortes Rey; Kamalveer Kaur Garewal; Paul Lukowicz

    Translating Videos into Synthetic Training Data for Wearable Sensor-Based Activity Recognition Systems Using Residual Deep Convolutional Networks.

    In: Applied Sciences, Vol. 11, No. 7, Pages 1-31, MDPI, 2021.

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