Computational Advantages of Deep Prototype-Based Learning

Thomas Hecht 1 Alexander Gepperth 2, 1
2 Flowers - Flowing Epigenetic Robots and Systems
Inria Bordeaux - Sud-Ouest, U2IS - Unité d'Informatique et d'Ingénierie des Systèmes
Abstract : We present a deep prototype-based learning architecture which achieves a performance that is competitive to a conventional, shallow prototype-based model but at a fraction of the computational cost, especially w.r.t. memory requirements. As prototype-based classification and regression methods are typically plagued by the exploding number of prototypes necessary to solve complex problems, this is an important step towards efficient prototype-based classification and regression. We demonstrate these claims by benchmarking our deep prototype-based model on the well-known MNIST dataset.
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Conference papers
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Submitted on : Friday, December 16, 2016 - 1:19:05 PM
Last modification on : Wednesday, July 3, 2019 - 10:48:05 AM
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Thomas Hecht, Alexander Gepperth. Computational Advantages of Deep Prototype-Based Learning. International Conference on Artificial Neural Networks (ICANN), 2016, Barcelona, Spain. pp.121 - 127, ⟨10.1007/978-3-319-44781-0_15⟩. ⟨hal-01418135⟩



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