Recurrent Neural Networks (RNNs) are artificial neural network models that are well-suited
for pattern classification tasks whose inputs and outputs are sequences. The importance of …

Sequence prediction and classification are ubiquitous and challenging problems in machine
learning that can require identifying complex dependencies between temporally distant …

Countless learning tasks require awareness of time. Image captioning, speech synthesis,
and video game playing all require that a model generate sequences of outputs. In other …

After their inception in the late 1980s, recurrent neural networks (RNNs) as a sequence
computing model have seen mushrooming interests in communities of natural language …

Many machine learning tasks can be expressed as the transformation---or\emph
{transduction}---of input sequences into output sequences: speech recognition, machine …

Neural networks have become increasingly popular for the task of language modeling.
Whereas feed-forward networks only exploit a fixed context length to predict the next word of …

Recurrent neural networks (RNNs) are neural network architectures with hidden state and
which use feedback loops to process a sequence of data that ultimately informs the final …

Recurrent neural networks (RNNs) have proved effective at one dimensional sequence
learning tasks, such as speech and online handwriting recognition. Some of the properties …

Abstract Recurrent Neural Networks (RNNs) are very powerful sequence models that do not
enjoy widespread use because it is extremely difficult to train them properly. Fortunately …

Abstract Recurrent Neural Network Language Models (RNN-LMs) have recently shown
exceptional performance across a variety of applications. In this paper, we modify the …