Transcription of LONG - Sepp Hochreiter
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LONG - Sepp Hochreiter
long SHORT-TERM MEMORY. Neural Computation 9(8):1735{1780, 1997. Sepp Hochreiter J urgen Schmidhuber Fakult at f ur Informatik IDSIA. Technische Universit at M unchen Corso Elvezia 36. 80290 M unchen, Germany 6900 Lugano, Switzerland ~hochreit ~juergen Abstract Learning to store information over extended time intervals via recurrent backpropagation takes a very long time, mostly due to insu cient, decaying error back ow. We brie y review Hochreiter 's 1991 analysis of this problem, then address it by introducing a novel, e cient, gradient-based method called \ long Short-Term Memory" (LSTM). Truncating the gradient where this does not do harm, LSTM can learn to bridge minimal time lags in excess of 1000.}
to e solv long time lag problems. (2) It has fully connected second-order sigma-pi units, while the LSTM hitecture's arc MUs are used only to gate access t constan error
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