Generative LSTM classifiers under post-training quantization are far more sensitive than discriminative ones to calibration data class balance and input noise, especially at 3- to 5-bit widths.
Pattern recognition and machine learning
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Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness
Generative LSTM classifiers under post-training quantization are far more sensitive than discriminative ones to calibration data class balance and input noise, especially at 3- to 5-bit widths.