12 Publikationen

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  • [12]
    2020 | Bielefelder E-Dissertation | PUB-ID: 2943362 OA
    Panzner, M. (2020). Learning action models by curiosity driven self exploration and language guided generalization. Bielefeld: Universität Bielefeld. doi:10.4119/unibi/2943362.
    PUB | PDF | DOI
     
  • [11]
    2019 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2933998 OA
    Panzner, M., Cimiano, P. & Gaspers, J. (2019). Modelling the co-emergence of linguistic constructions and action concepts: the case of action verbs. IEEE Transactions on Cognitive and Developmental Systems, 11(3), 435-449. IEEE. doi:10.1109/TCDS.2019.2900418.
    PUB | PDF | DOI | WoS
     
  • [10]
    2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2911974 OA
    Panzner, M. & Cimiano, P. (2017). A Deep Reinforcement Learning Based Model Supporting Object Familiarization. Proceedings of the 7th Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics. Gehalten auf der International Conference on Development and Learning and on Epigenetic Robotics, Lisbon, Portugal. doi:10.1109/DEVLRN.2017.8329828.
    PUB | PDF | DOI
     
  • [9]
    2017 | Kurzbeitrag Konferenz / Poster | Veröffentlicht | PUB-ID: 2914356 OA
    Panzner, M. & Cimiano, P. (2017). A Deep Reinforcement Learning Based Model Supporting Object Familiarization. Gehalten auf der International Conference on Development and Learning and on Epigenetic Robotics, Lisbon, Portugal.
    PUB | PDF
     
  • [8]
    2016 | Datenpublikation | PUB-ID: 2904362 OA
    Panzner, M. (2016). TLS Dataset. Bielefeld University. doi:10.4119/unibi/2904362.
    PUB | Dateien verfügbar | DOI
     
  • [7]
    2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2903474 OA
    Panzner, M. & Cimiano, P. (2016). Comparing Hidden Markov Models and Long Short Term Memory Neural Networks for Learning Action Representations (Lecture Notes in Computer Science). In P.M. Pardalos, P. Conca, G. Giuffrida & G. Nicosia (Hrsg.), Machine Learning, Optimization, and Big Data : Second International Workshop, MOD 2016, Volterra, Italy, August 26-29, 2016. Revised Selected Papers (S. 94-105). Gehalten auf der MOD 2016, Cham: Springer International Publishing. doi:10.1007/978-3-319-51469-7_8.
    PUB | PDF | DOI
     
  • [6]
    2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2775414 OA
    Panzner, M. & Cimiano, P. (2015). Incremental learning of action models as HMMs over qualitative trajectory representations (S. 76-84). Gehalten auf der Workshop on New Challenges in Neural Computation (NC2).
    PUB | PDF
     
  • [5]
    2015 | Kurzbeitrag Konferenz / Poster | Veröffentlicht | PUB-ID: 2785577 OA
    Panzner, M. & Cimiano, P. (2015). Incremental learning of action models as HMMs over qualitative trajectory representations. Gehalten auf der Workshop on New Challenges in Neural Computation (NC2).
    PUB | PDF
     
  • [4]
    2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2733058 OA
    Panzner, M., Gaspers, J. & Cimiano, P. (2015). Learning linguistic constructions grounded in qualitative action models. IEEE International Symposium on Robot and Human Interactive Communication. Gehalten auf der RO-MAN2015.
    PUB | PDF
     
  • [3]
    2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2659127 OA
    Gaspers, J., Panzner, M., Lemme, A., Cimiano, P., Rohlfing, K. & Wrede, S. (2014). A multimodal corpus for the evaluation of computational models for (grounded) language acquisition. EACL Workshop on Cognitive Aspects of Computational Language Learning. Gehalten auf der EACL Workshop on Cognitive Aspects of Computational Language Learning.
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  • [2]
    2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2624505 OA
    Panzner, M., Beyer, O. & Cimiano, P. (2013). Human Activity Classification with Online Growing Neural Gas. Workshop on New Challenges in Neural Computation (NC2) (S. 106-113).
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  • [1]
    2013 | Bielefelder Masterarbeit | PUB-ID: 2703072 OA
    Panzner, M. (2013). Online Klassifikation menschlicher Aktionen anhand von Space-Time Interest-Points. Bielefeld: Universität Bielefeld.
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