厦大纪荣嵘的paper
1.由于二值化,权重的符号对收敛起到较大作用,而一部分权重的符号难以改变限制了bnn的表达,即:死权重
2.所以引入了RECU恢复死权重的活性,(行为是权重标准化,研究了阈值
![avatar][base64str1]
具体公式是这个。t的最优取值是0.82
3.然而还有另一个影响因子 权重的信息熵。 大量使用正则化使得信息熵不可控。
b取绝对值的和的平均值,小于e-1的值不太能体现性能,所以需要扩大b的值(行为还是标准化,根据拉普拉斯定理,取根号2很不错,所以就乘一个根号2,每次更新后:
- 扩大t增加信息熵但是超过0.82会出现不可控的量化误差,这是固有的矛盾,作者使用了一种指数调度器,在训练时灵活调整t,以求帕累托最优。
对于预测来说,只要加入处理权重的那几句代码,其他的不变。
代码有问题没办法复现。
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