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Automatic model redundancy reduction for fast back-propagation for deep neural networks in speech recognition.

, , , and . IJCNN, page 1-6. IEEE, (2015)

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Quantifying Exposure Bias for Neural Language Generation., , , and . CoRR, (2019)Reshaping deep neural network for fast decoding by node-pruning., , , , and . ICASSP, page 245-249. IEEE, (2014)Exposure Bias versus Self-Recovery: Are Distortions Really Incremental for Autoregressive Text Generation?, , , and . EMNLP (1), page 5087-5102. Association for Computational Linguistics, (2021)Joint Energy-based Model Training for Better Calibrated Natural Language Understanding Models., , , and . EACL, page 1754-1761. Association for Computational Linguistics, (2021)An investigation on DNN-derived bottleneck features for GMM-HMM based robust speech recognition., , , and . ChinaSIP, page 30-34. IEEE, (2015)Automatic model redundancy reduction for fast back-propagation for deep neural networks in speech recognition., , , and . IJCNN, page 1-6. IEEE, (2015)Paragraph vector based topic model for language model adaptation., , , and . INTERSPEECH, page 3516-3520. ISCA, (2015)Exploiting LSTM structure in deep neural networks for speech recognition., and . ICASSP, page 5445-5449. IEEE, (2016)Detecting egregious responses in neural sequence-to-sequence models., and . CoRR, (2018)From Data Quality to Model Quality: an Exploratory Study on Deep Learning., , , , and . CoRR, (2019)