About: Long short-term memory

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Long short-term memory (LSTM) is an artificial recurrent neural network (RNN) architecture used in the field of deep learning. Unlike standard feedforward neural networks, LSTM has feedback connections. It can process not only single data points (such as images), but also entire sequences of data (such as speech or video). For example, LSTM is applicable to tasks such as unsegmented, connected handwriting recognition, speech recognition and anomaly detection in network traffic or IDSs (intrusion detection systems).

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rdf:type	Thing company
rdfs:label	Long short-term memory (en)
rdfs:comment	Long short-term memory (LSTM) is an artificial recurrent neural network (RNN) architecture used in the field of deep learning. Unlike standard feedforward neural networks, LSTM has feedback connections. It can process not only single data points (such as images), but also entire sequences of data (such as speech or video). For example, LSTM is applicable to tasks such as unsegmented, connected handwriting recognition, speech recognition and anomaly detection in network traffic or IDSs (intrusion detection systems). (en)
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sameAs	Long short-term memory Long short-term memory
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Subject	Artificial neural networks
gold:hypernym	Architecture
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Link from a Wikipage to another Wikipage	Round-off error TIMIT Activation function Alex Graves (computer scientist) AlphaStar (software) Amazon Polly Anomaly detection Apple Worldwide Developers Conference Backpropagation Backpropagation through time Bill Gates Semantic parsing Sepp Hochreiter Seq2seq Statistical classification University of Waterloo Business process management Artificial neural networks Differentiable neural computer Drug design Legendre polynomials Object co-segmentation Dalle Molle Institute for Artificial Intelligence Research DeepMind Deep learning Hyperbolic functions Jürgen Schmidhuber Phoneme Prediction Prefrontal cortex basal ganglia working memory Robot control Time aware long short-term memory Time series Connectionist temporal classification Convolution Convolutional neural network Cornell University Hidden Markov model Highway network Michigan State University OpenAI Spectral radius Speech recognition Electronic data processing Gated recurrent unit Google Neural Machine Translation Gradient descent IBM Research Feedforward neural network Felix Gers Hadamard product (matrices) Handwriting recognition International Conference on Document Analysis and Recognition Transportation forecasting Vanishing gradient problem StarCraft II Long-term potentiation Neuroevolution Recurrent neural network S Curve Sign language dbr:Switchboard_corpus
has abstract	Long short-term memory (LSTM) is an artificial recurrent neural network (RNN) architecture used in the field of deep learning. Unlike standard feedforward neural networks, LSTM has feedback connections. It can process not only single data points (such as images), but also entire sequences of data (such as speech or video). For example, LSTM is applicable to tasks such as unsegmented, connected handwriting recognition, speech recognition and anomaly detection in network traffic or IDSs (intrusion detection systems). A common LSTM unit is composed of a cell, an input gate, an output gate and a forget gate. The cell remembers values over arbitrary time intervals and the three gates regulate the flow of information into and out of the cell. LSTM networks are well-suited to classifying, processing and making predictions based on time series data, since there can be lags of unknown duration between important events in a time series. LSTMs were developed to deal with the vanishing gradient problem that can be encountered when training traditional RNNs. Relative insensitivity to gap length is an advantage of LSTM over RNNs, hidden Markov models and other sequence learning methods in numerous applications. (en)
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is Wikipage redirect of	Long short term memory LSTM LSTMs
is Link from a Wikipage to another Wikipage of	Machine learning in video games OCRopus 1-2-AX working memory task 1 the Road Activity recognition Alex Graves (computer scientist) Amazon Alexa

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