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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.
An LSTM unit contains three gates: An input gate, which controls the flow of new information into the memory cell; A forget gate, which controls how much information is retained from the previous time step; An output gate, which controls how much information is passed to the next layer. The equations for LSTM are: [2]
[7] [8] In 1933, Lorente de Nó discovered "recurrent, reciprocal connections" by Golgi's method, and proposed that excitatory loops explain certain aspects of the vestibulo-ocular reflex. [ 9 ] [ 10 ] During 1940s, multiple people proposed the existence of feedback in the brain, which was a contrast to the previous understanding of the neural ...
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]
This difference in gradient magnitude might introduce instability in the training process, slow it, or halt it entirely. [1] For instance, consider the hyperbolic tangent activation function. The gradients of this function are in range [-1,1]. The product of repeated multiplication with such gradients decreases exponentially.
Time Aware LSTM (T-LSTM) is a long short-term memory (LSTM) unit capable of handling irregular time intervals in longitudinal patient records. T-LSTM was developed by researchers from Michigan State University, IBM Research, and Cornell University and was first presented in the Knowledge Discovery and Data Mining (KDD) conference. [1]
Connectionist temporal classification (CTC) is a type of neural network output and associated scoring function, for training recurrent neural networks (RNNs) such as LSTM networks to tackle sequence problems where the timing is variable. It can be used for tasks like on-line handwriting recognition [1] or recognizing phonemes in speech audio ...
It is a bidirectional LSTM which takes character-level as inputs and produces word-level embeddings, trained on a corpus of about 30 million sentences and 1 billion words. The architecture of ELMo accomplishes a contextual understanding of tokens .