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Found 3 papers, 2 papers with code
Balanced Quantization: An Effective and Efficient Approach to Quantized Neural Networks
Overall, our method improves the prediction accuracies of QNNs without introducing extra computation during inference, has negligible impact on training speed, and is applicable to both Convolutional Neural Networks and Recurrent Neural Networks.
Effective Quantization Methods for Recurrent Neural Networks
In addition, we propose balanced quantization methods for weights to further reduce performance degradation.
DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients
We propose DoReFa-Net, a method to train convolutional neural networks that have low bitwidth weights and activations using low bitwidth parameter gradients.
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