Long short-term memory¶
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735-1780.
Cited by¶
1 citation across 1 artifact.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Foreseeing (Prediction)
- Modern deep-learning approaches to sequence prediction (Hochreiter and Schmidhuber's 1997 LSTM architecture
This sourceIntroduces the LSTM recurrent architecture for sequence learning; supports the claim that deep-learning sequence prediction shifted the paradigm from explicit mechanism-based models toward pattern-discovery in high-dimensional data.
Supported in partVerified against the publisher's abstract
The paper's own abstract states it introduces long short-term memory, a gradient-based recurrent method for learning over long time lags, supporting the LSTM attribution but not the 'deep learning' or paradigm-shift wording.
“We briefly review Hochreiter's (1991) analysis of this problem, then address it by introducing a novel, efficient, gradient based method called long short-term memory (LSTM).”
- Modern deep-learning approaches to sequence prediction (Hochreiter and Schmidhuber's 1997 LSTM architecture
Verification¶
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Registry ID ref:1a06f0df56a4 · see in the full table