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That is, LSTM can learn tasks that require memories of events that happened thousands or even millions of discrete time steps earlier. Problem-specific LSTM-like topologies can be evolved. [56] LSTM works even given long delays between significant events and can handle signals that mix low and high-frequency components.
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.
The Transformer architecture, being modular, allows variations. Several common variations are described here. [61] An "encoder-only" Transformer applies the encoder to map an input text into a sequence of vectors that represent the input text. This is usually used for text embedding and representation learning for downstream applications.
It was termed intra-attention [31] where an LSTM is augmented with a memory network as it encodes an input sequence. These strands of development were brought together in 2017 with the Transformer architecture , published in the Attention Is All You Need paper.
Jamba is a novel architecture built on a hybrid transformer and mamba SSM architecture developed by AI21 Labs with 52 billion parameters, making it the largest Mamba-variant created so far. It has a context window of 256k tokens. [13]
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]
Shannon's diagram of a general communications system, showing the process by which a message sent becomes the message received (possibly corrupted by noise). seq2seq is an approach to machine translation (or more generally, sequence transduction) with roots in information theory, where communication is understood as an encode-transmit-decode process, and machine translation can be studied as a ...
The Echo State Network (ESN) [4] belongs to the Recurrent Neural Network (RNN) family and provide their architecture and supervised learning principle. Unlike Feedforward Neural Networks, Recurrent Neural Networks are dynamic systems and not functions.