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The Long Short-Term Memory (LSTM) cell can process data sequentially and keep its hidden state through time. Long short-term memory (LSTM) [1] is a type of recurrent neural network (RNN) aimed at mitigating the vanishing gradient problem [2] commonly encountered by traditional RNNs.
Gated recurrent units (GRUs) are a gating mechanism in recurrent neural networks, introduced in 2014 by Kyunghyun Cho et al. [1] The GRU is like a long short-term memory (LSTM) with a gating mechanism to input or forget certain features, [2] but lacks a context vector or output gate, resulting in fewer parameters than LSTM. [3]
Around 2006, bidirectional LSTM started to revolutionize speech recognition, outperforming traditional models in certain speech applications. [ 38 ] [ 39 ] They also improved large-vocabulary speech recognition [ 3 ] [ 4 ] and text-to-speech synthesis [ 40 ] and was used in Google voice search , and dictation on Android devices . [ 41 ]
The computational effort for the softmax became a major limiting factor in the development of larger neural language models, motivating various remedies to reduce training times. [ 9 ] [ 10 ] Approaches that reorganize the softmax layer for more efficient calculation include the hierarchical softmax and the differentiated softmax . [ 9 ]
Other variants of echo state networks seek to change the formulation to better match common models of physical systems, such as those typically those defined by differential equations. Work in this direction includes echo state networks which partially include physical models, [ 13 ] hybrid echo state networks, [ 14 ] and continuous-time echo ...
A recursive neural network is a kind of deep neural network created by applying the same set of weights recursively over a structured input, to produce a structured prediction over variable-size input structures, or a scalar prediction on it, by traversing a given structure in topological order.
The following is a Python implementation of BatchNorm for 2D convolutions: import numpy as np def batchnorm_cnn ( x , gamma , beta , epsilon = 1e-9 ): # Calculate the mean and variance for each channel. mean = np . mean ( x , axis = ( 0 , 1 , 2 ), keepdims = True ) var = np . var ( x , axis = ( 0 , 1 , 2 ), keepdims = True ) # Normalize the ...
As hand-crafting weights defeats the purpose of machine learning, the model must compute the attention weights on its own. Taking analogy from the language of database queries, we make the model construct a triple of vectors: key, query, and value. The rough idea is that we have a "database" in the form of a list of key-value pairs.