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A residual neural network (also referred to as a residual network or ResNet) [1] is a deep learning architecture in which the layers learn residual functions with reference to the layer inputs. It was developed in 2015 for image recognition , and won the ImageNet Large Scale Visual Recognition Challenge ( ILSVRC ) of that year.
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DenseNets is a moinker used for a specific way to implement residual neural networks. If the link text had been "dense networks" it could have made sense to link to an opposite. Jeblad ( talk ) 20:51, 6 March 2019 (UTC) [ reply ]
Gated recurrent units (GRUs) are a gating mechanism in recurrent neural networks, introduced in 2014 by Kyunghyun Cho et al. [1] The GRU is like a long short-term memory (LSTM) with a gating mechanism to input or forget certain features, [2] but lacks a context vector or output gate, resulting in fewer parameters than LSTM. [3]
Residual in the bankruptcy of insolvent businesses, moneys that are left after all assets are sold and all creditors paid, to be divided among residual claimants Residual (or balloon) in finance, a lump sum owed to the financier at the end of a loan's term; for example Balloon payment mortgage
The Indonesian Wikipedia (Indonesian: Wikipedia bahasa Indonesia, WBI for short) is the Indonesian language edition of Wikipedia. It is the fifth-fastest-growing Asian-language Wikipedia after the Japanese, Chinese, Korean, and Turkish language Wikipedias. It ranks 25th in terms of depth among Wikipedias.
: neural network parameters. In words, it is a neural network that maps an input into an output , with the hidden vector playing the role of "memory", a partial record of all previous input-output pairs. At each step, it transforms input to an output, and modifies its "memory" to help it to better perform future processing.
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