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Publications

Displaying results 291 to 300 of 661.

  1. Daan Wierstra; Alexander Förster; Jan Peters; Jürgen Schmidhuber

    Solving Deep Memory POMDPs with Recurrent Policy Gradients

    In: Joaquim Marques de Sá; Luís A. Alexandre; Wlodzislaw Duch; Danilo P. Mandic (Hrsg.). Artificial Neural Networks - ICANN 2007, 17th International Conference, Proceedings. International Conference on Artificial Neural Networks (ICANN-2007), September 9-13, Porto, Portugal, Pages 697-706, Lecture Notes in Computer Science, Vol. 4668, Springer, 2007.

  2. Hymalai Bello; Bo Zhou; Sungho Suh; Paul Lukowicz

    MoCapaci: Posture and gesture detection in loose garments using textile cables as capacitive antennas

    In: ACM International Symposium on Wearable Computers. ACM International Symposium on Wearable Computers (ISWC-2021), September 21-26, ACM, 2021.

  3. Nijat Mehdiyev; Johannes Lahann; Andreas Emrich; David Enke; Peter Fettke; Peter Loos

    Time Series Classification using Deep Learning for Process Planning: A Case from the Process Industry

    In: Procedia Computer Science. Complex Adaptive Systems (CAS-2017), USA, Pages 242-249, Vol. 114, 2017.

  4. Babu Ajish; Frank Kirchner

    Terrain Adaption Controller for a Walking Excavator Robot using Deep Reinforcement Learning

    In: 2021 20th International Conference on Advanced Robotics (ICAR). International Conference On Advanced Robotics (ICAR-2021), December 7-10, Ljubljana, Slovenia, Pages 64-70, IEEE Xplore, ieeexplore.ieee.org/document/9659399, 12/2021.

  5. Jens Popper; Carsten Harms; Martin Ruskowski

    Enabling reliable Visual Quality Control in Smart Factories through TSN

    In: Roberto Teti; Doriana M. D'Addona (Hrsg.). Procedia CIRP, Vol. 88 - 13th CIRP Conference on Intelligent Computation in Manufacturing Engineering, 17-19 July 2019, Gulf of Naples, Italy, Pages 549-553, Elsevier B.V. 2020.

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. 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.

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