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Publications

Displaying results 411 to 420 of 571.

  1. Manuela Schuler; Richard Membarth; Philipp Slusallek

    XEngine: Optimal Tensor Rematerialization for Neural Networks in Heterogeneous Environments

    In: David Kaeli (Hrsg.). ACM Transactions on Architecture and Code Optimization (TACO), Vol. 20, No. 1, Pages 17:1-17:25, ACM, 12/2022.

  2. Dayananda Herurkar; Timur Sattarov; Jörn Hees; Sebastian Palacio; Federico Raue; Andreas Dengel

    Cross-Domain Transformation for Outlier Detection on Tabular Datasets

    In: IJCNN 2023 : International Joint Conference on Neural Networks. International Joint Conference on Neural Networks (IJCNN-2023), located at IJCNN, June 18-23, Gold Coast Convention and Exhibition Centre, Queensland, Australia, DFKI Research Reports (RR), IEEE Xplore, 2023.

  3. Wei Jiang; Hans Dieter Schotten

    Deep Learning-Aided Delay-Tolerant Zero-Forcing Precoding in Cell-Free Massive MIMO

    In: Proceedings of the 2022 IEEE 96th Vehicular Technology Conference. IEEE Vehicular Technology Conference (VTC-2022), IEEE VTC 2022 Fall, September 26-29, London, United Kingdom, IEEE, 9/2022.

  4. Ajish Babu; 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. Viktor Eisenstadt; Hardik Arora; Christoph Ziegler; Jessica Bielski; Christoph Langenhan; Klaus-Dieter Althoff; Andreas Dengel

    Comparative Evaluation of Tensor-based Data Representations for Deep Learning Methods in Architecture

    In: V. Stojakovic; B. Tepavcevic (Hrsg.). Towards a new, configurable architecture - Proceedings of the 39th eCAADe Conference - Volume 1. Education and Research in Computer Aided Architectural Design in Europe (eCAADe-2021), September 8-10, Novi Sad, Serbia, Pages 45-54, CUMINCAD, 2021.

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