… are replaced by memory blocks, as illus… memory cells to store and access information over
long periods of time, thereby mitigating the vanishing gradient problem. For example, as long …

… (short-term memory, as opposed to long-term memory … learning what to put in shortterm
memory, however, take too … and corresponding teacher signals are long. Although theoretically …

… As for the search criteria on which this literature review is based, we evaluated 409 papers
containing the terms “Long short-term memory” or “LSTM” in either the title, abstract or …

Long Short-Term Memory Recurrent Neural Networks (LSTM-RNN) are one of the most
powerful dynamic classifiers publicly known. The network itself and the related learning …

… LSTM: 1 input unit, 1 input at a time (memory overhead) … Needs short- term memory to …

… First, we focus on long short-term memory (LSTM) networks, one of the most advanced …
The LSTM network outperforms the memory-free methods with statistically and economically …

… As an alternative to the standard architecture, we proposed the Long Short-Term Memory
Projected (LSTMP) architecture to address the computational complexity of learning LSTM …

… First, we explore passing a long-term feature [xt−10,...,xt] to the CNN, and a shortterm … 1%
relative improvement over passing just the long-term feature from the CNN into the LSTM. …

… memory tape that explicitly simulates the human memory span… memory addressing mechanism
at every time step. In the following, we first review the standard Long Short-Term Memory …

… Long Short Term Memory (LSTM) networks have been demonstrated to be particularly …
longer term patterns of unknown length, due to their ability to maintain long term memory. …