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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. File:Residual network data structures in Android devices (IA ...

    en.wikipedia.org/wiki/File:Residual_network_data...

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  4. Residual network - Wikipedia

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

    Download as PDF; Printable version; ... move to sidebar hide. From Wikipedia, the free encyclopedia. Redirect page. Redirect to: Flow network#Residuals; Retrieved ...

  5. AlexNet - Wikipedia

    en.wikipedia.org/wiki/AlexNet

    AlexNet block diagram. AlexNet is a convolutional neural network (CNN) architecture, designed by Alex Krizhevsky in collaboration with Ilya Sutskever and Geoffrey Hinton, who was Krizhevsky's Ph.D. advisor at the University of Toronto in 2012. It had 60 million parameters and 650,000 neurons. [1]

  6. 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 ...

  7. File:Network flow residual SVG.svg - Wikipedia

    en.wikipedia.org/wiki/File:Network_flow_residual...

    This image is a derivative work of the following images: File:Network_flow_residual.png licensed with Cc-by-sa-3.0-migrated, GFDL . 2006-03-19T19:58:39Z Maksim 332x164 (23838 Bytes) La bildo estas kopiita de wikipedia:en.

  8. Vanishing gradient problem - Wikipedia

    en.wikipedia.org/wiki/Vanishing_gradient_problem

    Residual connections, or skip connections, refers to the architectural motif of +, where is an arbitrary neural network module. This gives the gradient of ∇ f + I {\displaystyle \nabla f+I} , where the identity matrix do not suffer from the vanishing or exploding gradient.

  9. 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]