2.7k citations · 8.5k across the 41 of their papers we have counts for
Showing 2015Show all
3 papers · 1 filter
cs.LG2015★ 473 cited
Semi-supervised Sequence Learning
Andrew M. Dai, Quoc V. Le
We present two approaches that use unlabeled data to improve sequence learning with recurrent networks. The first approach is to predict what comes next in a sequence, which is a c…
cs.LG2015
Generating Sentences from a Continuous Space
Samuel R. Bowman, Luke Vilnis, Oriol Vinyals +3
The standard recurrent neural network language model (RNNLM) generates sentences one word at a time and does not work from an explicit global sentence representation. In this work,…
cs.CL2015★ 263 cited
Document Embedding with Paragraph Vectors
Andrew M. Dai, Christopher Olah, Quoc V. Le
Paragraph Vectors has been recently proposed as an unsupervised method for learning distributed representations for pieces of texts. In their work, the authors showed that the meth…