Learning to store information over extended time intervals by recurrent backpropagation takes
a very long time, mostly because of insufficient, decaying error backflow. We briefly review …

… long time lag recurrent net project, since 2002 assistant professor in Berlin) … LSTM: 1
input unit, 1 input at a time (memory overhead) … • LSTM may offer a better solution by …

… We then use long short term memory (LSTM), our own recent algorithm, to solve hard problems
that can neither be quickly solved by random weight guessing nor by any other recurrent …

… Feedforward and recurrent neural network layers (namely Long Short-Term Memory; LSTM)
are stacked to form a single structure which is trained by back-propagating error gradients …

Previous work on learning regular languages from exemplary training sequences showed
that Long Short- Term Memory (LSTM) outperforms traditional recurrent neural networks (…

… In this article, however, we show that even LSTM fails to learn to process certain very long
… to span long time lags must also address the issue of forgetting in short-term memory (unit …

… The approach uses a bidirectional recurrent neural network with long short-term memory
blocks. We use a recently introduced objective function, known as Connectionist Temporal …

… We focus on Long Short-Term Memory (LSTM) because it usually outperforms other
RNNs. Surprisingly, LSTM augmented by “peephole connections” from its internal cells to its …

… RNNs, the deepest of all NNs, may learn to solve problems of potentially unlimited depth,
for example, by learning to store in their activation-based "short-term memory" representations …

… 2.2 Long Short-Term Memory. LSTM is a recurrent neural network purposely designed to
learn long-… The unique feature of the LSTM architecture is the memory cell, which is capable of …