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  2. Residual neural network - Wikipedia

    en.wikipedia.org/wiki/Residual_neural_network

    A residual block in a deep residual network. Here, the residual connection skips two layers. 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.

  3. 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.

  4. 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 ...

  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. Here's why preeclampsia remains one of the most worrisome ...

    www.aol.com/heres-why-preeclampsia-remains-one...

    Preeclampsia is an especially important condition to be diagnosed by a professional during routine prenatal visits because "it can be totally asymptomatic - meaning you may not even know that you ...

  7. Transformer (deep learning architecture) - Wikipedia

    en.wikipedia.org/wiki/Transformer_(deep_learning...

    For many years, sequence modelling and generation was done by using plain recurrent neural networks (RNNs). A well-cited early example was the Elman network (1990). In theory, the information from one token can propagate arbitrarily far down the sequence, but in practice the vanishing-gradient problem leaves the model's state at the end of a long sentence without precise, extractable ...

  8. Why Trump has so much riding on the House speaker vote - AOL

    www.aol.com/why-trump-much-riding-house...

    I mean, that would just be a big, beautiful exclamation point.” The nation’s new leaders plan sweeping overhauls of immigration policy and big tax cuts using complex maneuvers required to ...

  9. What is the debt ceiling, and is Trump right that a default ...

    www.aol.com/debt-ceiling-trump-default-could...

    In other words, it doesn't have a real meaning other than you've violated something," Trump told Karl. "And that may be just, you know, one day, half a story, or it may lead to the depression of ...