Authors
Ilya Sutskever, Oriol Vinyals, Quoc V Le
Publication date
2014
Conference
Advances in neural information processing systems
Pages
3104-3112
Description
Abstract Deep Neural Networks (DNNs) are powerful models that have achieved excellent
performance on difficult learning tasks. Although DNNs work well whenever large labeled
training sets are available, they cannot be used to map sequences to sequences. In this
paper, we present a general end-to-end approach to sequence learning that makes minimal
assumptions on the sequence structure. Our method uses a multilayered Long Short-Term
Memory (LSTM) to map the input sequence to a vector of a fixed dimensionality, and then ...
performance on difficult learning tasks. Although DNNs work well whenever large labeled
training sets are available, they cannot be used to map sequences to sequences. In this
paper, we present a general end-to-end approach to sequence learning that makes minimal
assumptions on the sequence structure. Our method uses a multilayered Long Short-Term
Memory (LSTM) to map the input sequence to a vector of a fixed dimensionality, and then ...
Total citations
Scholar articles
I Sutskever, O Vinyals, QV Le - Advances in neural information processing systems, 2014
Dates and citation counts are estimated and are determined automatically by a computer program.