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2021年4月16日 · Batch normalization (BN) is a key facilitator and considered essential for state-of-the-art binary neural networks (BNN). However, the BN layer is costly to calculate and is typically implemented with non-binary parameters, leaving a hurdle for the efficient implementation of BNN training.

Batch normalization (BN) is a key facilitator and considered essential for state-of-the-art binary neural networks (BNN). However, the BN layer is costly to calculate and is typically implemented with non-binary parameters, leaving a hurdle for the efficient implementation of BNN training.

因此， 贝叶斯神经网络 (BNN, Bayesian Neural Networks)的提出主要用于防止过拟合，提升模型的预测灵活性，提供不确定性的估计能力等方面。 思想 传统神经网络，服从最大似然估计(MLE)，即权重在不同取值之间具有相同的可能性：

本文将总结贝叶斯神经网络，首先，我将简单介绍一下什么是贝叶斯神经网络（bnn）；接着我将介绍bnn是怎么训练的，bnn是怎么预测的；最后，我会介绍bnn背后的运作原理。

2024年12月11日 · 该压缩文件包含了基于PYNQ复现的二值神经网络（BNN）重建项目，项目名称为cnvW1A1。该项目整合了高层次综合（HLS）和Vivado工程项目。BNN是一种特殊的神经网络，其中网络的权重和激活函数均被量化为二值值（-1或+1）...

2020年10月12日 · 本文题目为 《Binarized Neural Networks: Training Neural Networks with Weights and Activations Constrained to +1 or −1》 本文提出了一种新的轻量级网络——二值化网络。 二值化网络即将网络中权重固定为-1和+1两个值，feather map也要进行二值化处理。 从空间角度来看，原先保存的权重都是32bit的浮点数，现在变成了1bit的精度，节省了32倍的储存空间；从时间角度来看，+1与-1之间的乘法运算可以利用同或运算代替，这可以大大的提高运算速 …

1. 介绍了一种训练BNN的方法，在训练阶段使用二值化的权重和激活值计算参数梯度； 2. 基于 Torch7 和 Theano 框架完成了两个实验，实验结果表明在 MNIST 、 CIFAR-10 和 SVHN 数据集上训练 二值化神经网络 是可能的，而且都达到了最先进的结果； 3.

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