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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.
In machine learning, a neural network (also artificial neural network or neural net, abbreviated ANN or NN) is a model inspired by the structure and function of biological neural networks in animal brains. [1] [2] An ANN consists of connected units or nodes called artificial neurons, which loosely model the neurons in the brain. Artificial ...
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.
In machine learning, the Highway Network was the first working very deep feedforward neural network with hundreds of layers, much deeper than previous neural networks. [ 1 ] [ 2 ] [ 3 ] It uses skip connections modulated by learned gating mechanisms to regulate information flow, inspired by long short-term memory (LSTM) recurrent neural networks .
He is an associate professor at Massachusetts Institute of Technology and is known as one of the creators of residual neural network (ResNet). [1] [3]
In 2015, two techniques were developed concurrently to train very deep networks: highway network [102] and residual neural network (ResNet). [103] The ResNet research team attempted to train deeper ones by empirically testing various tricks for training deeper networks until they discovered the deep residual network architecture. [104]
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The network in AlphaGo Zero is a ResNet with two heads. [1]: Appendix: Methods The stem of the network takes as input a 17x19x19 tensor representation of the Go board. 8 channels are the positions of the current player's stones from the last eight time steps. (1 if there is a stone, 0 otherwise.