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• W2910694009 abstract "Deep artificial neural networks are feed-forward architectures capable of very impressive performances in diverse domains. Indeed stacking multiple layers allows a hierarchical composition of local functions, providing efficient compact mappings. Compared to the brain, however, such architectures are closer to a single pipeline and require huge amounts of data, while concrete cases for either human or machine learning systems are often restricted to not-so-big data sets. Furthermore, interpretability of the obtained results is a key issue: since deep learning applications are increasingly present in society, it is important that the underlying processes be accessible and understandable to every one. In order to target these challenges, in this contribution we analyze how considering prototypes in a rather generalized sense (with respect to the state of the art) allows to reasonably work with small data sets while providing an interpretable view of the obtained results. Some mathematical interpretation of this proposal is discussed. Sensitivity to hyperparameters is a key issue for reproducible deep learning results, and is carefully considered in our methodology. Performances and limitations of the proposed setup are explored in details, under different hyperparameters sets, in an analogous way as biological experiments are conducted. We obtain a rather simple architecture, easy to explain, and which allows, combined with a standard method, to target both performances and interpretability." @default.
• W2910694009 created "2019-01-25" @default.
• W2910694009 creator A5019886872 @default.
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• W2910694009 date "2019-01-09" @default.
• W2910694009 modified "2023-09-27" @default.
• W2910694009 title "Bio-inspired Analysis of Deep Learning on Not-So-Big Data Using Data-Prototypes" @default.
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• W2910694009 doi "https://doi.org/10.3389/fncom.2018.00100" @default.
• W2910694009 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6333865" @default.
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• W2910694009 hasPublicationYear "2019" @default.
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