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  • Rectified Linear Unit, otherwise known as ReLU is an activation function used in neural networks.[1]
  • It suffers from the problem of dying ReLU’s.[1]
  • Does the Rectified Linear Unit (ReLU) function meet this criterion?[2]
  • Because ReLU doesn't change any non-negative value.[3]
  • So for (sigmoid, relu) in the last two layers, the model is not able to learn, i.e. the gradients are not back propagated well.[4]
  • Rectifier linear unit or its more widely known name as ReLU becomes popular for the past several years since its performance and speed.[5]
  • However, ReLU destroys gradient vanishing problem.[5]
  • That’s why, experiments show ReLU is six times faster than other well known activation functions.[5]
  • If you input an x-value that is greater than zero, then it's the same as the ReLU – the result will be a y-value equal to the x-value.[6]
  • SNNs cannot be derived with (scaled) rectified linear units (ReLUs), sigmoid units, tanh units, and leaky ReLUs.[6]
  • ReLU is very simple to calculate, as it involves only a comparison between its input and the value 0.[7]
  • As a consequence, the usage of ReLU helps to prevent the exponential growth in the computation required to operate the neural network.[7]
  • While sigmoidal functions have derivatives that tend to 0 as they approach positive infinity, ReLU always remains at a constant 1.[7]
  • This flowchart shows a typical architecture for a CNN with a ReLU and a Dropout layer.[7]
  • larization to the inputs of the ReLU can be reduced.[8]
  • Instead of sigmoids, most recent deep learning networks use rectified linear units (ReLUs) for the hidden layers.[9]
  • ReLU activations are the simplest non-linear activation function you can use, obviously.[9]
  • Research has shown that ReLUs result in much faster training for large networks.[9]
  • That is, the ReLU units can irreversibly die during training since they can get knocked off the data manifold.[9]
  • Neural networks (NN) with rectified linear units (ReLU) have been widely implemented since 2012.[10]
  • In this paper, we describe an activation function called the biased ReLU neuron (BReLU), which is similar to the ReLU.[10]
  • ReLu is a non-linear activation function that is used in multi-layer neural networks or deep neural networks.[11]
  • According to equation 1, the output of ReLu is the maximum value between zero and the input value.[11]
  • ReLU stands for rectified linear activation unit and is considered one of the few milestones in the deep learning revolution.[12]
  • The activations functions that were used mostly before ReLU such as sigmoid or tanh activation function saturated.[12]
  • ReLU, on the other hand, does not face this problem as its slope doesn’t plateau, or “saturate,” when the input gets large.[12]
  • Because the slope of ReLU in the negative range is also 0, once a neuron gets negative, it’s unlikely for it to recover.[12]
  • ReLU stands for Rectified Linear Unit.[13]
  • This is another variant of ReLU that aims to solve the problem of gradient’s becoming zero for the left half of the axis.[13]
  • The parameterised ReLU, as the name suggests, introduces a new parameter as a slope of the negative part of the function.[13]
  • Unlike the leaky relu and parametric ReLU functions, instead of a straight line, ELU uses a log curve for defning the negatice values.[13]
  • One way ReLUs improve neural networks is by speeding up training.[14]
  • The Rectified Linear Unit has become very popular in the last few years.[15]
  • (-) Unfortunately, ReLU units can be fragile during training and can “die”.[15]
  • Leaky ReLUs are one attempt to fix the “dying ReLU” problem.[15]
  • Instead of the function being zero when x < 0, a leaky ReLU will instead have a small negative slope (of 0.01, or so).[15]
  • Since ReLU is zero for all negative inputs, it’s likely for any given unit to not activate at all.[16]
  • As long as not all of them are negative, we can still get a slope out of ReLU.[16]
  • If not, leaky ReLU and ELU are also good alternatives to try.[16]
  • ReLU stands for rectified linear unit, and is a type of activation function.[17]
  • Concatenated ReLU has two outputs, one ReLU and one negative ReLU, concatenated together.[17]
  • You may run into ReLU-6 in some libraries, which is ReLU capped at 6.[17]
  • On the other hand, ELU becomes smooth slowly until its output equal to -α whereas RELU sharply smoothes.[18]
  • ReLu is less computationally expensive than tanh and sigmoid because it involves simpler mathematical operations.[18]
  • Further reading Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification, Kaiming He et al.[18]
  • A node or unit that implements this activation function is referred to as a rectified linear activation unit, or ReLU for short.[19]
  • The idea is to use rectified linear units to produce the code layer.[19]
  • Most papers that achieve state-of-the-art results will describe a network using ReLU.[19]
  • … we propose a new generalization of ReLU, which we call Parametric Rectified Linear Unit (PReLU).[19]