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  2. Residual neural network - Wikipedia

    en.wikipedia.org/wiki/Residual_neural_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.

  3. Flow network - Wikipedia

    en.wikipedia.org/wiki/Flow_network

    The residual capacity of an arc e with respect to a pseudo-flow f is denoted c f, and it is the difference between the arc's capacity and its flow. That is, c f (e) = c(e) - f(e). From this we can construct a residual network, denoted G f (V, E f), with a capacity function c f which models the amount of available capacity on the set of arcs in ...

  4. Ford–Fulkerson algorithm - Wikipedia

    en.wikipedia.org/wiki/Ford–Fulkerson_algorithm

    The Ford–Fulkerson method or Ford–Fulkerson algorithm (FFA) is a greedy algorithm that computes the maximum flow in a flow network.It is sometimes called a "method" instead of an "algorithm" as the approach to finding augmenting paths in a residual graph is not fully specified [1] or it is specified in several implementations with different running times. [2]

  5. Residual network - Wikipedia

    en.wikipedia.org/?title=Residual_network&redirect=no

    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.

  6. Gated recurrent unit - Wikipedia

    en.wikipedia.org/wiki/Gated_recurrent_unit

    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]

  7. Graph neural network - Wikipedia

    en.wikipedia.org/wiki/Graph_neural_network

    The graph convolutional network (GCN) was first introduced by Thomas Kipf and Max Welling in 2017. [9] A GCN layer defines a first-order approximation of a localized spectral filter on graphs. GCNs can be understood as a generalization of convolutional neural networks to graph-structured data. The formal expression of a GCN layer reads as follows:

  8. U2's Larry Mullen Jr. says dyscalculia affects his drumming ...

    www.aol.com/u2s-larry-mullen-jr-says-211302785.html

    "I'm trying to count the bars," he explained, and with dyscalculia, "counting bars is like climbing Everest." U2's Sphere concert film is staggeringly lifelike. We talk to the Edge about its creation

  9. AlexNet - Wikipedia

    en.wikipedia.org/wiki/AlexNet

    The codebase for AlexNet was released under a BSD license, and had been commonly used in neural network research for several subsequent years. [ 20 ] [ 17 ] In one direction, subsequent works aimed to train increasingly deep CNNs that achieve increasingly higher performance on ImageNet.