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Residual Neural Network
A residual neural network (ResNet) is an artificial neural network (ANN). It is a gateless or open-gated variant of the HighwayNet, the first working very deep feedforward neural network with hundreds of layers, much deeper than previous neural networks. ''Skip connections'' or ''shortcuts'' are used to jump over some layers ( HighwayNets may also learn the skip weights themselves through an additional weight matrix for their gates). Typical ''ResNet'' models are implemented with double- or triple- layer skips that contain nonlinearities (ReLU) and batch normalization in between. Models with several parallel skips are referred to as ''DenseNets''. In the context of residual neural networks, a non-residual network may be described as a ''plain network''. Like in the case of Long Short-Term Memory recurrent neural networks there are two main reasons to add skip connections: to avoid the problem of vanishing gradients, thus leading to easier to optimize neural networks, where the ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
ResNets
A residual neural network (ResNet) is an artificial neural network (ANN). It is a gateless or open-gated variant of the HighwayNet, the first working very deep feedforward neural network with hundreds of layers, much deeper than previous neural networks. ''Skip connections'' or ''shortcuts'' are used to jump over some layers ( HighwayNets may also learn the skip weights themselves through an additional weight matrix for their gates). Typical ''ResNet'' models are implemented with double- or triple- layer skips that contain nonlinearities (ReLU) and batch normalization in between. Models with several parallel skips are referred to as ''DenseNets''. In the context of residual neural networks, a non-residual network may be described as a ''plain network''. Like in the case of Long Short-Term Memory recurrent neural networks there are two main reasons to add skip connections: to avoid the problem of vanishing gradients, thus leading to easier to optimize neural networks, where the ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Feature (machine Learning)
In machine learning and pattern recognition, a feature is an individual measurable property or characteristic of a phenomenon. Choosing informative, discriminating and independent features is a crucial element of effective algorithms in pattern recognition, classification and regression. Features are usually numeric, but structural features such as strings and graphs are used in syntactic pattern recognition. The concept of "feature" is related to that of explanatory variable used in statistical techniques such as linear regression. Classification A numeric feature can be conveniently described by a feature vector. One way to achieve binary classification is using a linear predictor function (related to the perceptron) with a feature vector as input. The method consists of calculating the scalar product between the feature vector and a vector of weights, qualifying those observations whose result exceeds a threshold. Algorithms for classification from a feature vector incl ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Computational Statistics
Computational statistics, or statistical computing, is the bond between statistics and computer science. It means statistical methods that are enabled by using computational methods. It is the area of computational science (or scientific computing) specific to the mathematical science of statistics. This area is also developing rapidly, leading to calls that a broader concept of computing should be taught as part of general statistical education. As in traditional statistics the goal is to transform raw data into knowledge, Wegman, Edward J. āComputational Statistics: A New Agenda for Statistical Theory and Practice.€¯ Journal of the Washington Academy of Sciences', vol. 78, no. 4, 1988, pp. 310ā€“322. ''JSTOR'' but the focus lies on computer intensive statistical methods, such as cases with very large sample size and non-homogeneous data sets. The terms 'computational statistics' and 'statistical computing' are often used interchangeably, although Carlo Lauro (a former presid ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
