398 citations · 870 across the 15 of their papers we have counts for
4 papers · 1 filter
Adding Gradient Noise Improves Learning for Very Deep Networks
Arvind Neelakantan, Luke Vilnis, Quoc V. Le +4
Deep feedforward and recurrent networks have achieved impressive results in many perception and language processing applications. This success is partially attributed to architectu…
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,…
Learning Dynamic Feature Selection for Fast Sequential Prediction
Emma Strubell, Luke Vilnis, Kate Silverstein +1
We present paired learning and inference algorithms for significantly reducing computation and increasing speed of the vector dot products in the classifiers that are at the heart…
Bethe Projections for Non-Local Inference
Luke Vilnis, David Belanger, Daniel Sheldon +1
Many inference problems in structured prediction are naturally solved by augmenting a tractable dependency structure with complex, non-local auxiliary objectives. This includes the…