NICE learns a composition of invertible neural-network layers that transform data into independent latent variables, enabling exact log-likelihood training and sampling for density estimation.
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An attention-based encoder-decoder model achieves English-to-French translation performance comparable to phrase-based systems by automatically learning soft alignments.
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NICE: Non-linear Independent Components Estimation
NICE learns a composition of invertible neural-network layers that transform data into independent latent variables, enabling exact log-likelihood training and sampling for density estimation.
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Neural Machine Translation by Jointly Learning to Align and Translate
An attention-based encoder-decoder model achieves English-to-French translation performance comparable to phrase-based systems by automatically learning soft alignments.