Thomas Hecht, Alexander Gepperth. Computational Advantages of Deep Prototype-Based Learning. In Alessandro E. P. Villa, Paolo Masulli, Antonio Javier Pons Rivero, editors, Artificial Neural Networks and Machine Learning - ICANN 2016 - 25th International Conference on Artificial Neural Networks, Barcelona, Spain, September 6-9, 2016, Proceedings, Part II. Volume 9887 of Lecture Notes in Computer Science, pages 121-127, Springer, 2016. [doi]
@inproceedings{HechtG16, title = {Computational Advantages of Deep Prototype-Based Learning}, author = {Thomas Hecht and Alexander Gepperth}, year = {2016}, doi = {10.1007/978-3-319-44781-0_15}, url = {http://dx.doi.org/10.1007/978-3-319-44781-0_15}, researchr = {https://researchr.org/publication/HechtG16}, cites = {0}, citedby = {0}, pages = {121-127}, booktitle = {Artificial Neural Networks and Machine Learning - ICANN 2016 - 25th International Conference on Artificial Neural Networks, Barcelona, Spain, September 6-9, 2016, Proceedings, Part II}, editor = {Alessandro E. P. Villa and Paolo Masulli and Antonio Javier Pons Rivero}, volume = {9887}, series = {Lecture Notes in Computer Science}, publisher = {Springer}, isbn = {978-3-319-44780-3}, }