Search results
Results From The WOW.Com Content Network
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.
This page was last edited on 20 November 2017, at 05:18 (UTC).; Text is available under the Creative Commons Attribution-ShareAlike 4.0 License; additional terms may apply.
In May 2015, Rupesh Kumar Srivastava, Klaus Greff, and Schmidhuber used LSTM principles to create the highway network, a feedforward neural network with hundreds of layers, much deeper than previous networks. [8] [31] [32] In Dec 2015, the residual neural network (ResNet) was published, which is a variant of the highway network. [30] [33]
I have no idea why DenseNets are linked to Sparse network. 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 20:51, 6 March 2019 (UTC)
The residual is the difference between the observed value and the estimated value of the quantity of interest (for example, a sample mean). The distinction is most important in regression analysis , where the concepts are sometimes called the regression errors and regression residuals and where they lead to the concept of studentized residuals .
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.
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]
Pseudocode is commonly used in textbooks and scientific publications related to computer science and numerical computation to describe algorithms in a way that is accessible to programmers regardless of their familiarity with specific programming languages.