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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.
English: A diagram for a one-unit Long Short-Term Memory (LSTM). From bottom to top : input state, hidden state and cell state, output state. Gates are sigmoïds or hyperbolic tangents. Other operators : element-wise plus and multiplication. Weights are not displayed. Inspired from Understanding LSTM, Blog of C. Olah
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]
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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).
Bowers also has a shot at the most yards for a season by a rookie tight end — Mike Ditka's 1.076 during the 1961 season. But he might have to pick up his production a bit to overtake that mark ...
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