$\partial\mathbb{B}$ nets are differentiable neural networks that learn
discrete boolean-valued functions by gradient descent. $\partial\mathbb{B}$
nets have two semantically equivalent aspects: a differentiable soft-net, with
real weights, and a non-differentiable hard-net, with boolean weights. We train
the soft-net by backpropagation and then `harden' the learned weights to yield
boolean weights that bind with the hard-net. The result is a learned discrete
function. `Hardening' involves no loss of accuracy, unlike existing approaches
to neural network binarization. Preliminary experiments demonstrate that
$\partial\mathbb{B}$ nets achieve comparable performance on standard machine
learning problems yet are compact (due to 1-bit weights) and interpretable (due
to the logical nature of the learnt functions).

该研究介绍了一种利用梯度下降学习离散布尔值函数的可微分神经网络，该网络具有不可微分的硬网络和可微分的软网络，可以通过后向传播算法进行训练。在实验中，该网络展现了可比较的性能，同时由于使用了 1 比特权重的方式，紧凑并且具有解释性。