Hash embeddings for efficient word representations
We present hash embeddings, an efficient method for representing words in a continuous vector form.
Authors: Anders Boesen Lindbo Larsen, Søren Kaae Sønderby, Hugo Larochelle, Ole Winther
We present hash embeddings, an efficient method for representing words in a continuous vector form.
Deep generative models trained with large amounts of unlabelled data have proven to be powerful within the domain of unsupervised learning.
Humans possess an ability to abstractly reason about objects and their interactions, an ability not shared with state-of-the-art deep learning models