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- W1504070387 abstract "We have recently shown that when initiated with small weights, recurrent neural networks (RNNs) with standard sigmoid-type activation functions are inherently biased towards Markov models, i.e. even prior to any training, RNN dynamics can be readily used to extract finite memory machines [6,8]. Following [2], we refer to this phenomenon as the architectural bias of RNNs. In this paper we further extend our work on the architectural bias in RNNs by performing a rigorous fractal analysis of recurrent activation patterns.We obtain both lower and upper bounds on various types of fractal dimensions, such as box-counting and Hausdorff dimensions." @default.
- W1504070387 created "2016-06-24" @default.
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- W1504070387 date "2002-01-01" @default.
- W1504070387 modified "2023-10-18" @default.
- W1504070387 title "Architectural Bias in Recurrent Neural Networks — Fractal Analysis" @default.
- W1504070387 cites W2133688510 @default.
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- W1504070387 doi "https://doi.org/10.1007/3-540-46084-5_219" @default.
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