PUB - Publikationen an der Universität Bielefeld

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• [12]
  

  2021 | Kurzbeitrag Konferenz / Poster | Veröffentlicht | PUB-ID: 2960159

  Moringen, A., Fleer, S., & Ritter, H., 2021. Meta-learning Haptic Exploration of Simple 3D Objects. In 2021 IEEE World Haptics Conference. Piscataway, NJ: IEEE.

  PUB | PDF
   
• [11]
  

  2020 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2949926

  Mohanty, S.P., et al., 2020. Deep Learning for Understanding Satellite Imagery: An Experimental Survey. Frontiers in Artificial Intelligence, 3: 534696.

  PUB | PDF | DOI | WoS | PubMed | Europe PMC
   
• [10]
  

  2020 | Bielefelder E-Dissertation | PUB-ID: 2949123

  Fleer, S., 2020. Scaffolding for learning from reinforcement: Improving interaction learning, Bielefeld: Universität Bielefeld.

  PUB | Dateien verfügbar | DOI
   
• [9]
  

  2020 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2943876

  Moringen, A., et al., 2020. Attention-based Robot Learning of Haptic Interaction. In I. Nisky, et al., eds. Haptics: Science, Technology, Applications. 12th International Conference, EuroHaptics 2020, Leiden, The Netherlands, September 6–9, 2020, Proceedings. Lecture Notes in Computer Science. no.12272 Cham: Springer, pp. 462-470.

  PUB | PDF | DOI | Download (ext.)
   
• [8]
  

  2020 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2939850

  Fleer, S., et al., 2020. Learning efficient haptic shape exploration with a rigid tactile sensor array. PLOS ONE, 15(1): e0226880.

  PUB | PDF | DOI | WoS | PubMed | Europe PMC | arXiv
   
• [7]
  

  2019 | Datenpublikation | PUB-ID: 2934182

  Fleer, S., 2019. Supplementary Material - Solving a tool-based interaction task using deep reinforcement learning with visual attention, Bielefeld University.

  PUB | Dateien verfügbar | DOI
   
• [6]
  

  2019 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2935994

  Fleer, S., & Ritter, H., 2019. Solving a Tool-Based Interaction Task Using Deep Reinforcement Learning with Visual Attention. In Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization. WSOM 2019. Advances in Intelligent Systems and Computing . no.976 Cham: Springer, pp. 231-240.

  PUB | DOI | Download (ext.)
   
• [5]
  

  2019 | Datenpublikation | PUB-ID: 2936475

  Fleer, S., et al., 2019. Supplementary Material - Learning efficient haptic shape exploration with a rigid tactile sensor array, Bielefeld University.

  PUB | Dateien verfügbar | DOI
   
• [4]
  

  2019 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2939854

  Moringen, A., Fleer, S., & Ritter, H., 2019. Scaffolding Haptic Attention with Controller Gating. In I. V. Tetko, et al., eds. Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation.28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019, Proceedings, Part I. Lecture Notes in Computer Science. no.11727 Cham: Springer, pp. 669-684.

  PUB | DOI | Download (ext.)
   
• [3]
  

  2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2933709

  Fleer, S., & Ritter, H., 2019. Skill Transfer for Mediated Interaction Learning. In 2018 IEEE-RAS 18th International Conference on Humanoid Robots (Humanoids). Piscataway, NJ: IEEE.

  PUB | DOI | Download (ext.)
   
• [2]
  

  2019 | Konferenzbeitrag | PUB-ID: 2933988

  Melnik, A., et al., 2019. Modularization of End-to-End Learning: Case Study in Arcade Games. In 32nd Conference on Neural Information Processing Systems (NeurIPS 2018), Workshop on Causal Learning.

  PUB | arXiv
   
• [1]
  

  2017 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2914841

  Fleer, S., & Ritter, H., 2017. Comparing Action Sets: Mutual Information as a Measure of Control. In Artificial Neural Networks and Machine Learning – ICANN 2017. Lecture Notes in Computer Science. Cham: Springer International Publishing, pp. 68-75.

  PUB | DOI