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The gated recurrent unit (GRU) simplifies the LSTM. [3] Compared to the LSTM, the GRU has just two gates: a reset gate and an update gate. GRU also merges the cell state and hidden state. The reset gate roughly corresponds to the forget gate, and the update gate roughly corresponds to the input gate. The output gate is removed. There are ...
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]
[59] [60] They have fewer parameters than LSTM, as they lack an output gate. [61] Their performance on polyphonic music modeling and speech signal modeling was found to be similar to that of long short-term memory. [62] There does not appear to be particular performance difference between LSTM and GRU. [62] [63]
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.
Its architecture consists of two parts. The encoder is an LSTM that takes in a sequence of tokens and turns it into a vector. The decoder is another LSTM that converts the vector into a sequence of tokens. Similarly, another 130M-parameter model used gated recurrent units (GRU) instead of LSTM. [22]
A RNN (often a LSTM) where a series is decomposed into a number of scales where every scale informs the primary length between two consecutive points. A first order scale consists of a normal RNN, a second order consists of all points separated by two indices and so on. The Nth order RNN connects the first and last node.
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