79 citations · 83 across the 7 of their papers we have counts for
3 papers · 1 filter
Probabilistic FastText for Multi-Sense Word Embeddings
Ben Athiwaratkun, Andrew Gordon Wilson, Anima Anandkumar
We introduce Probabilistic FastText, a new model for word embeddings that can capture multiple word senses, sub-word structure, and uncertainty information. In particular, we repre…
There Are Many Consistent Explanations of Unlabeled Data: Why You Should Average
Ben Athiwaratkun, Marc Finzi, Pavel Izmailov +1
Presently the most successful approaches to semi-supervised learning are based on consistency regularization, whereby a model is trained to be robust to small perturbations of its…
Hierarchical Density Order Embeddings
Ben Athiwaratkun, Andrew Gordon Wilson
By representing words with probability densities rather than point vectors, probabilistic word embeddings can capture rich and interpretable semantic information and uncertainty. T…