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  2. Residual neural network - Wikipedia

    en.wikipedia.org/wiki/Residual_neural_network

    A basic block is the simplest building block studied in the original ResNet. [1] This block consists of two sequential 3x3 convolutional layers and a residual connection. The input and output dimensions of both layers are equal. Block diagram of ResNet (2015). It shows a ResNet block with and without the 1x1 convolution.

  3. AlphaGo Zero - Wikipedia

    en.wikipedia.org/wiki/AlphaGo_Zero

    The body is a ResNet with either 20 or 40 residual blocks and 256 channels. There are two heads, a policy head and a value head. Policy head outputs a logit array of size 19 × 19 + 1 {\displaystyle 19\times 19+1} , representing the logit of making a move in one of the points, plus the logit of passing .

  4. Leela Zero - Wikipedia

    en.wikipedia.org/wiki/Leela_Zero

    8 channels are the positions of the other player's stones from the last eight time steps. 1 channel is all 1 if black is to move, and 0 otherwise. 1 channel is all 1 if white is to move, and 0 otherwise. (This channel is not present in the original AlphaGo Zero) The body is a ResNet with 40 residual blocks and 256 channels.

  5. Kaiming He - Wikipedia

    en.wikipedia.org/wiki/Kaiming_He

    His 2016 paper Deep Residual Learning for Image Recognition is the most cited research paper in 5 years according to Google Scholar's reports in 2020 and 2021. [ 7 ] [ 8 ] Awards and recognitions

  6. Inception (deep learning architecture) - Wikipedia

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

    Inception v2 was released in 2015, in a paper that is more famous for proposing batch normalization. [7] [8] It had 13.6 million parameters.It improves on Inception v1 by adding batch normalization, and removing dropout and local response normalization which they found became unnecessary when batch normalization is used.

  7. 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.

  8. File:ResNet block.svg - Wikipedia

    en.wikipedia.org/wiki/File:ResNet_block.svg

    You are free: to share – to copy, distribute and transmit the work; to remix – to adapt the work; Under the following conditions: attribution – You must give appropriate credit, provide a link to the license, and indicate if changes were made.

  9. Residual block termination - Wikipedia

    en.wikipedia.org/wiki/Residual_block_termination

    In cryptography, residual block termination is a variation of cipher block chaining mode (CBC) that does not require any padding. It does this by effectively changing to cipher feedback mode for one block .