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Publications

Displaying results 331 to 340 of 569.

  1. Michael Lutter; Christian Ritter; Jan Peters

    Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning

    In: 7th International Conference on Learning Representations. International Conference on Learning Representations (ICLR-2019), May 6-9, New Orleans, LA, USA, OpenReview.net, 2019.

  2. Andrew S. Morgan; Daljeet Nandha; Georgia Chalvatzaki; Carlo D'Eramo; Aaron M. Dollar; Jan Peters

    Model Predictive Actor-Critic: Accelerating Robot Skill Acquisition with Deep Reinforcement Learning

    In: IEEE International Conference on Robotics and Automation. IEEE International Conference on Robotics and Automation (ICRA-2021), May 30 - June 5, Xi'an, China, Pages 6672-6678, IEEE, 2021.

  3. Michael Lutter; Shie Mannor; Jan Peters; Dieter Fox; Animesh Garg

    Value Iteration in Continuous Actions, States and Time

    In: Marina Meila; Tong Zhang (Hrsg.). Proceedings of the 38th International Conference on Machine Learning. International Conference on Machine Learning (ICML-2021), July 18-24, Pages 7224-7234, Proceedings of Machine Learning Research, Vol. 139, PMLR, 2021.

  4. Riad Akrour; Asma Atamna; Jan Peters

    Convex optimization with an interpolation-based projection and its application to deep learning

    In: Machine Learning, Vol. 110, No. 8, Pages 2267-2289, Springer, 2021.

  5. Carlo D'Eramo; Andrea Cini; Alessandro Nuara; Matteo Pirotta; Cesare Alippi; Jan Peters; Marcello Restelli

    Gaussian Approximation for Bias Reduction in Q-Learning

    In: Journal of Machine Learning Research, Vol. 22, Pages 277:1-277:51, JMLR, 2021.

  6. Daniel Tanneberg; Alexandros Paraschos; Jan Peters; Elmar Rueckert

    Deep spiking networks for model-based planning in humanoids

    In: 16th IEEE-RAS International Conference on Humanoid Robots. IEEE-RAS International Conference on Humanoid Robots (Humanoids-2016), November 15-17, Cancun, Mexico, Pages 656-661, IEEE, 2016.

  7. Dinh Nam Pham; Vera Czehmann; Eleftherios Avramidis

    Disambiguating Signs: Deep Learning-based Gloss-level Classification for German Sign Language by Utilizing Mouth Actions

    In: Proceedings of the 31st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN-2023), October 4-6, Bruges, Belgium, ISBN 978-2-87587-088-9. i6doc.com publ. 10/2023.

  8. Amos Smith; Jeremy Coffelt; Kai Lingemann

    A Deep Learning Framework for Semantic Segmentation of Underwater Environments

    In: OCEANS 22 Hampton Roads. OCEANS MTS/IEEE Conference (OCEANS-2022), October 17-20, Hampton Roads, VA, USA, IEEE, 10/2022.

  9. Michael Lutter; Boris Belousov; Kim Listmann; Debora Clever; Jan Peters

    HJB optimal feedback control with deep differential value functions and action constraints

    In: Leslie Pack Kaelbling; Danica Kragic; Komei Sugiura (Hrsg.). 3rd Annual Conference on Robot Learning. Conference on Robot Learning (CoRL-2019), October 30 - November 1, Osaka, Japan, Pages 640-650, Proceedings of Machine Learning Research (PMLR), Vol. 100, PMLR, 2019.

  10. Michael Lutter; Debora Clever; Boris Belousov; Kim Listmann; Jan Peters

    Evaluating the Robustness of HJB Optimal Feedback Control

    In: International Symposium on Robotics. International Symposium on Robotics (ISR-2020), 52th, December 9-10, Pages 1-8, VDE, 2020.

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