https://arxiv.org/pdf/1410.5401.pdf?fbclid=IwAR1mft01ZW2dGNGyVczj4HR6E5H8xKpqTlniZeuI8JxGuSNzOdoNupq4j9M

San Diego State University

RNNs come in many variants, including:
 - Bidirectional RNNs
 - Memory Units (LSTMs) RNNs
 - Fully recurrent 
 - Elman/Jordan networks, or Simple Recurrent Networks (SRN)
 - Recursive neural network
 - Hopfield
 - Echo State Network (ESN)
 - Stacked RNNs
 - Hierarchical
 - Neural Turing machines (NTMs)
 - Differentiable Neural Computer (DNC) 
 - Recurrent Multi-Layer Perceptron (RMLP)
 - Independent RNN (IndRNN)
 - Neural history compressor 
 - Second order RNNs 
 - Gated recurrent unit (GRU)
 - Continuous-Time Recurrent Neural Network (CTRNN)
 - Multiple Timescales Recurrent Neural Network (MTRNN) 
 - Neural Network Pushdown Automata (NNPDA)

RNN Extensions and Types

A bidirectional RNN assumes that the correct output not only depends on the previous inputs in the time series but also on future inputs. For example, in translation models, it is often necessary to use a word from the end of the source sentence in order to predict a word early in the target sentence. 

To make this possible, two RNNs are stacked on top of each other, one going from beginning to end and the other from end to beginning, and the output is computed based on hidden states of both networks.

Bidirectional RNNs

Stacked RNNs consist of multiple networks where the output of one layer serves as the input to a subsequent layer. The initial layers of stacked networks can induce representations that serve as useful abstractions for further layers — representations that might prove difficult to induce in a single RNN. So it can outperform single-layer networks.

Stacked RNNs

Gated Recurrent Units (GRUs) resolve the vanishing gradients problem in simple RNNs and also ease the burden of introducing a considerable number of additional parameters in the LSTMs, by dispensing with the use of a separate context vector, and by reducing the number of gates to 2 — a reset/relevance gate and an update gate.
The purpose of the reset gate is to decide which aspects of the previous hidden state are relevant to the current context and what can be ignored. It computes an intermediate representation for the new hidden state at the current time.
The purpose of the update gate is to determine which aspects of this new state will be used directly in the new hidden state and which aspects of the previous state need to be preserved for future use.

Gated recurrent unit (GRU)

NTMs(Neural Turing Machines) are neural network architectures that use external memory. These models are able to infer algorithmic tasks like copying, sorting, associative recall. According to the inventors of this architecture via NTMs they: "extend the capabilities of neural networks by coupling them to external memory resources, which they can interact with by attentional processes"
These models are relatively new compared to LSTMs and RNNs. They were created in 2014. 

Neural Turing Machines (NTM)

In the late 1990s, researchers Hochreiter and Schmidhuber proposed the Long Short-Term Memory (LSTM), which enables Recurrent Neural Networks (RNNs) to retain information over extended sequences rather than merely between consecutive time steps. Originally published in 1997, LSTMs gained significant recognition through victories in prediction competitions during the mid-2000s and became the dominant architecture for sequence learning from 2011 until the rise of Transformer models beginning in 2017. Even Transformers owe some of their key ideas to architectural design innovations first introduced by the LSTM. An LSTM-based RNN shares the same high-level architecture as a basic RNN (whether simple, bidirectional, or deep), but replaces standard activation functions with specialized LSTM cells.

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