We therefore propose Cross-View Training (CVT), a semi-supervised learning algorithm that improves the representations of a Bi-LSTM sentence encoder using a mix of labeled and unlabeled data.
SOTA for CCG Supertagging on CCGBank
This paper presents our 7th place solution to the second YouTube-8M video understanding competition which challenges participates to build a constrained-size model to classify millions of YouTube videos into thousands of classes.
For the IWSLT English-Vietnamese training, we obtain BLEU test/dev scores of 24. 0/21. 9 and 24. 2/21. 9 using core dimensions $(2, 2, 256) \times (2, 2, 512)$ with learning rate 0. 0012 and rank distributions $(1, 4, 4, 1)$ and $(1, 4, 16, 1)$ respectively.
To take advantage of the deep-learning method in detecting urban land-use patterns, we applied a transfer-learning-based remote-sensing image approach to extract and classify features.