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- Long Short-Term Memory Network - an overview - ScienceDirect
LSTM outperformed other models such as support vector regression (SVR), autoregressive integrated moving average (ARIMA), and bidirectional long, short term memory (Bi-LSTM) for the time series forecasting of confirmed infected cases, number of deaths and recoveries in different countries (Haider Abbass et al , 2021)
- A survey on long short-term memory networks for time series prediction
LSTM autoencoder The decoding and encoding of information is often realized in an autoencoder structure In [19] a stacked LSTM autoencoder solves the problem of high dimensional input sequences and the prediction of high dimensional parameter spaces by a reducing and an expanding network
- RNN-LSTM: From applications to modeling techniques and beyond . . .
LSTM has been specifically designed to address the issue of vanishing gradients, which makes vanilla RNNs unsuitable for learning long-term dependencies (Jaydip and Sidra, 2022) LSTMs possess the capacity to process sequential data and retain information from previous steps in the sequence, enabling them to predict future steps effectively
- Long Short-Term Memory - an overview | ScienceDirect Topics
Long short-term memory (LSTM) (Hochreiter and Schmidhuber, 1997) is a variant of recurrent neural networks (RNNs) that has already been discussed in Section 5 and illustrated in Fig 8 LSTM can be employed for feature extraction and for the classification of time-series data (Glowacz, 2023) Here, we explore the classification of ASD and TD by
- Working Memory Connections for LSTM - ScienceDirect
Since its introduction, LSTM has gained a lot of attention for its flexibility and efficacy in many different tasks To simplify the LSTM structure, Liu et al (2020) propose to exploit the content of the long-term memory cell in a recurrent block with only two gates However, this model neglects the importance of the LSTM output
- PI-LSTM: Physics-informed long short-term memory . . . - ScienceDirect
Nevertheless, as the number of training samples increased from 10 to 60, both LSTM and PI-LSTM exhibited enhanced accuracy, with LSTM experiencing a particularly noteworthy improvement The gap between LSTM and PI-LSTM decreased substantially, and these two models demonstrated highly similar performance after the number of training samples
- Interpretable spatio-temporal attention LSTM model for flood . . .
At the same time, SA-LSTM and TA-LSTM also performed better, which is better than LSTM model mostly At t + 1 , all the models perform well, and errors increase to different degrees with time Fig 12 is the R 2 results of different models, which suggests our proposed STA-LSTM model outperform others in most times on all datasets
- Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term . . .
Because of their effectiveness in broad practical applications, LSTM networks have received a wealth of coverage in scientific journals, technical blo…
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