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Found 6 papers, 1 papers with code
The Future of Combating Rumors? Retrieval, Discrimination, and Generation
Artificial Intelligence Generated Content (AIGC) technology development has facilitated the creation of rumors with misinformation, impacting societal, economic, and political ecosystems, challenging democracy.
Bayesian Neural Network Language Modeling for Speech Recognition
State-of-the-art neural network language models (NNLMs) represented by long short term memory recurrent neural networks (LSTM-RNNs) and Transformers are becoming highly complex.
Mixed Precision of Quantization of Transformer Language Models for Speech Recognition
Experiments conducted on Penn Treebank (PTB) and a Switchboard corpus trained LF-MMI TDNN system suggest the proposed mixed precision Transformer quantization techniques achieved model size compression ratios of up to 16 times over the full precision baseline with no recognition performance degradation.
Mixed Precision Low-bit Quantization of Neural Network Language Models for Speech Recognition
In order to overcome the difficulty in using gradient descent methods to directly estimate discrete quantized weights, alternating direction methods of multipliers (ADMM) are used to efficiently train quantized LMs.
Low-bit Quantization of Recurrent Neural Network Language Models Using Alternating Direction Methods of Multipliers
Index Terms: Language models, Recurrent neural networks, Quantization, Alternating direction methods of multipliers.
Bayesian Transformer Language Models for Speech Recognition
Performance improvements were also obtained on a cross domain LM adaptation task requiring porting a Transformer LM trained on the Switchboard and Fisher data to a low-resource DementiaBank elderly speech corpus.
