In machine learning, the Highway Network was the first working very deep feedforward neural network with hundreds of layers, much deeper than previous artificial neural networks.
It uses skip connections modulated by learned gating mechanisms to regulate information flow, inspired by Long Short-Term Memory (LSTM) recurrent neural networks.
The advantage of a Highway Network over the common deep neural networks is that it solves or partially prevents the vanishing gradient problem, thus leading to easier to optimize neural networks.
The gating mechanisms facilitate information flow across many layers ("information highways").
Highway Networks have been used as part of text sequence labeling and speech recognition tasks.
An open-gated or gateless Highway Network variant called Residual neural network was used to win the ImageNet 2015 competition. This has become the most cited neural network of the 21st century.
The model has two gates in addition to the H(WH, x) gate: the transform gate T(WT, x) and the carry gate C(WC, x). Those two last gates are non-linear transfer functions (by convention Sigmoid function). The H(WH, x) function can be any desired transfer function.
The carry gate is defined as C(WC, x) = 1 - T(WT, x). While the transform gate is just a gate with a sigmoid transfer function.
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In machine learning, the vanishing gradient problem is encountered when training artificial neural networks with gradient-based learning methods and backpropagation. In such methods, during each iteration of training each of the neural networks weights receives an update proportional to the partial derivative of the error function with respect to the current weight. The problem is that in some cases, the gradient will be vanishingly small, effectively preventing the weight from changing its value.
Long short-term memory (LSTM) network is a recurrent neural network (RNN), aimed to deal with the vanishing gradient problem present in traditional RNNs. Its relative insensitivity to gap length is its advantage over other RNNs, hidden Markov models and other sequence learning methods. It aims to provide a short-term memory for RNN that can last thousands of timesteps, thus "long short-term memory".
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