Results 231 to 240 of about 335,967 (267)
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2012
As discussed in the previous chapter, an important benefit of recurrent neural networks is their ability to use contextual information when mapping between input and output sequences. Unfortunately, for standard RNN architectures, the range of context that can be in practice accessed is quite limited.
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As discussed in the previous chapter, an important benefit of recurrent neural networks is their ability to use contextual information when mapping between input and output sequences. Unfortunately, for standard RNN architectures, the range of context that can be in practice accessed is quite limited.
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Short-term, intermediate-term, and long-term memories
Behavioural Brain Research, 1993This paper focuses on the temporal dimension of memory formation and storage. Is the usual two-fold separation between short-term memory (STM) and long-term memory (LTM) sufficient to encompass all the phenomena of memory? The traditional view is that STM grades into LTM.
M R, Rosenzweig +4 more
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Short-Term Traffic Prediction Using Long Short-Term Memory Neural Networks
2018 IEEE International Congress on Big Data (BigData Congress), 2018Short-term traffic prediction allows Intelligent Transport Systems to proactively respond to events before they happen. With the rapid increase in the amount, quality, and detail of traffic data, new techniques are required that can exploit the information in the data in order to provide better results while being able to scale and cope with increasing
Zainab Abbas +3 more
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Improved Long Short-Term Memory Network Based Short Term Load Forecasting
2019 Chinese Automation Congress (CAC), 2019The power load sequence is a kind of complex nonlinear time series. As an improved RNN, the long short-term memory (LSTM) network has been applied to short term load forecasting because it can capture the temporal dynamic of nonlinear time series. In order to further improve the precision of load forecasting results, we establish the CEEMD-AE-LSTM load
Jie Cui, Qiang Gao, Dahua Li
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Short-Term Forecasting of Stock Prices Using Long Short Term Memory
2018 International Conference on Information Technology (ICIT), 2018Predicting stock market is not an easy task as it is a chaotic system i.e. whose dynamics are sensitive to arbitrarily small differences in initial conditions. Any small changes in the system can produce compound errors in predicting the future behavior of the system.
Saurav Kumar, Dhruba Ningombam
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Software effort estimation Based on long short term memory and stacked long short term memory
2022 8th International Conference on Contemporary Information Technology and Mathematics (ICCITM), 2022Farah B. Ahmad, Laheeb M. Ibrahim
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Cell-expanded Long Short-term Memory
2022 Joint 12th International Conference on Soft Computing and Intelligent Systems and 23rd International Symposium on Advanced Intelligent Systems (SCIS&ISIS), 2022Jun Rokui, Rin Adachi
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