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The independently recurrent neural network (IndRNN) [87] addresses the gradient vanishing and exploding problems in the traditional fully connected RNN. Each neuron in one layer only receives its own past state as context information (instead of full connectivity to all other neurons in this layer) and thus neurons are independent of each other ...
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 relative insensitivity to gap length is its advantage over other RNNs, hidden Markov models , and other sequence learning methods.
A problem with seq2seq models at this point was that recurrent neural networks are difficult to parallelize. The 2017 publication of Transformers [ 5 ] resolved the problem by replacing the encoding RNN with self-attention Transformer blocks ("encoder blocks"), and the decoding RNN with cross-attention causally-masked Transformer blocks ...
For recurrent neural networks, the long short-term memory (LSTM) network was designed to solve the problem (Hochreiter & Schmidhuber, 1997). [9]
Then, the backpropagation algorithm is used to find the gradient of the loss function with respect to all the network parameters. Consider an example of a neural network that contains a recurrent layer and a feedforward layer . There are different ways to define the training cost, but the aggregated cost is always the average of the costs of ...
A Hopfield network (or associative memory) is a form of recurrent neural network, or a spin glass system, that can serve as a content-addressable memory.The Hopfield network, named for John Hopfield, consists of a single layer of neurons, where each neuron is connected to every other neuron except itself.
One problem with seq2seq models was their use of recurrent neural networks, which are not parallelizable as both the encoder and the decoder processes the sequence token-by-token. The decomposable attention attempted to solve this problem by processing the input sequence in parallel, before computing a "soft alignment matrix" ("alignment" is ...
In neural networks, the gating mechanism is an architectural motif for controlling the flow of activation and gradient signals. They are most prominently used in recurrent neural networks (RNNs), but have also found applications in other architectures.