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Low-Bit Quantization Of Recurrent Neural Network Language Models Using Alternating Direction Methods Of Multipliers
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Low-Bit Quantization Of Recurrent Neural Network Language Models Using Alternating Direction Methods Of Multipliers
The high memory consumption and computational costs of Recurrent neural network language models (RNNLMs) limit their wider application on resource constrained devices. In recent years, neural network quantization techniques that are capable of producing e
The high memory consumption and computational costs of Recurrent neural network language models (RNNLMs) limit their wider application on resource constrained devices. In recent years, neural network quantization techniques that are capable of producing e
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