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RNN (LSTM) specifications


Unique identifier OMICS_29353
Alternative name Recurrent Neural Network for Long Short Term Memory
Software type Framework/Library
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux
Programming languages Keras
Computer skills Advanced
Stability Stable
Maintained Yes


No version available


  • person_outline Dawen Cai

Publication for Recurrent Neural Network for Long Short Term Memory

RNN (LSTM) citations


Emotion computing using Word Mover’s Distance features based on Ren_CECps

PLoS One
PMCID: 5889067
PMID: 29624573
DOI: 10.1371/journal.pone.0194136

[…] ng in social networks, some standard corpus based on weibo data had been published [–]. Inspired by the excellent performance of deep neural network in image recognition, a lot of researches based on RNN [], LSTM [], CNN [] for sentiment analysis had been done, works based on sentiment embeddings also get excellent results [] and will attract more and more attention.For estimating emotion of words […]


Automatic Modulation Classification Based on Deep Learning for Unmanned Aerial Vehicles

PMCID: 5876703
PMID: 29558434
DOI: 10.3390/s18030924

[…] eature extraction, CNNs cannot model the changes in time series well. As is known to us, the temporal property of data is important for AMC applications. As a variant of the recurrent neural network (RNN), LSTM uses the gate structure to realize information transfer in the network in time sequence, which reflects the depth in time series. Therefore, LSTM has a superior capacity to process the time […]


Social Image Captioning: Exploring Visual Attention and User Attention

PMCID: 5855536
PMID: 29470409
DOI: 10.3390/s18020646

[…] ed and φ() is an element-wise activation function.As illustrated in previous works, the long short-term memory (LSTM) achieves a better performance than vanilla RNN in image captioning. Compared with RNN, LSTM not only computes the hidden states, but also maintains a cell state to account for relevant signals that have been observed. They could modulate information to the cell state by gates.Given […]


IMU to Segment Assignment and Orientation Alignment for the Lower Body Using Deep Learning

PMCID: 5795510
PMID: 29351262
DOI: 10.3390/s18010302

[…] lowed by the simulation of noisy IMU data, where the noise was added to reduce the synthetic gap. For both the I2S assignment and alignment determination we utilized a suitable combination of CNN and RNN (LSTM and GRU) based neural network approach.Regarding the evaluation results on real IMU data, the most promising approach consisted of combining simulated and real IMU data for training, while w […]


Generative Recurrent Networks for De Novo Drug Design

Mol Inform
PMCID: 5836943
PMID: 29095571
DOI: 10.1002/minf.201700111

[…] We have successfully applied a generative RNN‐LSTM model for de novo design of chemical structures, and have demonstrated the model's applicability to (i) generating compound libraries for high‐throughput screening, (ii) hit‐to‐lead optimizat […]


Sleep Quality Prediction From Wearable Data Using Deep Learning

PMCID: 5116102
PMID: 27815231
DOI: 10.2196/mhealth.6562

[…] A subtype of RNN, LSTM uses specifically designed memory blocks as units in the recurrent layer to capture longer-range dependencies. The optimal configuration values for LSTM were a mini batch size of 5, dropout […]


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RNN (LSTM) institution(s)
Department of Cell and Developmental Biology, University of Michigan Medical School, Ann Arbor, MI, USA; Department of Electrical Engineering and Computer Science, University of Michigan School of Engineering, Ann Arbor, MI, USA; Department of Computer Science, Texas State University, San Marcos, TX, USA
RNN (LSTM) funding source(s)
Supported by Michigan miBRAIN initiative, a NIH/NIAI (R01AI130303), NSF/Neuronex-MINT (NSF-1707316), and by NIH/NIMH (R01MH110932).

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