“Approaches to convert RNNLM to BNLM”版本间的差异
来自cslt Wiki
（→related algorithm） |
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| 第7行： | 第7行： | ||
=related algorithm= | =related algorithm= | ||
* gibbs sampling[http://cos.name/2013/01/lda-math-mcmc-and-gibbs-sampling/] | * gibbs sampling[http://cos.name/2013/01/lda-math-mcmc-and-gibbs-sampling/] | ||
| + | * Kullback–Leibler divergence[http://zh.wikipedia.org/wiki/%E7%9B%B8%E5%AF%B9%E7%86%B5] | ||
2014年11月4日 (二) 13:41的版本
main paper
comparing approaches to convert recurrent neural networks into backoff language models for efficient decoding[1]
- VARIATIONAL APPROXIMATION OF LONG-SPAN LANGUAGE MODELS FOR LVCSR[2]
- Conversion of Recurrent Neural Network Language Models to Weighted Finite State Transducers for Automatic Speech Recognition[3]
- Converting Neural Network Language Models into Back-off Language Models for Efficient Decoding in Automatic Speech Recognition[4]
