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Why Gradient Clipping Accelerates Training: A Theoretical Justification for Adaptivity.

, , , and . ICLR, OpenReview.net, (2020)

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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)An investigation on DNN-derived bottleneck features for GMM-HMM based robust speech recognition., , , and . ChinaSIP, page 30-34. IEEE, (2015)Exploiting LSTM structure in deep neural networks for speech recognition., and . ICASSP, page 5445-5449. IEEE, (2016)A Systematic Characterization of Sampling Algorithms for Open-ended Language Generation., , , and . AACL/IJCNLP, page 334-346. Association for Computational Linguistics, (2020)Knowledge Crosswords: Geometric Reasoning over Structured Knowledge with Large Language Models., , , , , , and . CoRR, (2023)Paragraph vector based topic model for language model adaptation., , , and . INTERSPEECH, page 3516-3520. ISCA, (2015)SemStamp: A Semantic Watermark with Paraphrastic Robustness for Text Generation., , , , , , , , , and . CoRR, (2023)Negative Training for Neural Dialogue Response Generation., and . CoRR, (2019)Detecting Egregious Responses in Neural Sequence-to-sequence Models., and . ICLR (Poster), OpenReview.net, (2019)k-SemStamp: A Clustering-Based Semantic Watermark for Detection of Machine-Generated Text., , , , and . CoRR, (2024)