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2 papers

2013ICLR 2013 Workshop Track; arXiv preprint arXiv:1301.3781 — https://arxiv.org/abs/1301.3781

Efficient Estimation of Word Representations in Vector Space

Kai Chen, Greg Corrado, Jeffrey Dean, Tomas Mikolov

Finding: The models learned high-quality word vectors from 1.6 billion words in under a day, orders of magnitude faster than earlier neural approaches. Strikingly, the resulting vector space encoded semantic and syntactic relationships as directions: vector arithmetic such as king − man + woman produced a vector closest to queen, showing that regularities in language were captured as consistent geometric offsets.

vector-spaces word-embeddings

1986Nature, Vol. 323, pp. 533-536 — https://doi.org/10.1038/323533a0

Learning representations by back-propagating errors

Ronald J. Williams, Geoffrey E. Hinton, David E. Rumelhart

Finding: Hidden units learn to represent important task features on their own (e.g. 'person', 'generation', 'nationality' in a family-tree problem) — deep networks learn abstract representations, not just memorize.

backpropagation neural-networks