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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.
The standard LSTM architecture was introduced in 2000 by Felix Gers, Schmidhuber, and Fred Cummins. [20] Today's "vanilla LSTM" using backpropagation through time was published with his student Alex Graves in 2005, [21] [22] and its connectionist temporal classification (CTC) training algorithm [23] in 2006. CTC was applied to end-to-end speech ...
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]
Hochreiter developed the long short-term memory (LSTM) neural network architecture in his diploma thesis in 1991 leading to the main publication in 1997. [3] [4] LSTM overcomes the problem of numerical instability in training recurrent neural networks (RNNs) that prevents them from learning from long sequences (vanishing or exploding gradient).
After his PhD, Graves was postdoc working with Schmidhuber at the Technical University of Munich and Geoffrey Hinton [4] at the University of Toronto.. At the Dalle Molle Institute for Artificial Intelligence Research, Graves trained long short-term memory (LSTM) neural networks by a novel method called connectionist temporal classification (CTC). [5]
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 was created by researchers at the Allen Institute for Artificial Intelligence, [2] and University of Washington and first released in February, 2018. 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.
Speakman spent years training in American Kenpo under his principal instructor, Larry Tatum, as well as under the system's founder Parker. [5] Speakman received his first-degree black belt in American Kenpo in 1984. He was promoted to ninth degree in kenpo karate by Mills Crenshaw and Bob White and ninth in Gōjū-ryū by Lou Angel on July 2, 2013.