Reverse Derivative Ascent：一种学习布尔电路的分类方法
Reverse Derivative Ascent: A Categorical Approach to Learning Boolean Circuits
摘要：
我们介绍反向导数上升：一个分类模拟梯度为基础的方法，机器学习。我们的算法被定义在所谓的反向微分类别的水平。它可以用来学习模型的参数，这些参数被表示为这些类别的态射。我们激励的例子是布尔电路：我们展示了我们的算法可以应用到这样的电路，通过使用逆微分范畴理论。注意，我们的方法允许我们直接学习布尔电路的参数，与现有的二进制神经网络方法相反。此外，我们通过在基准机器学习数据集上的实验结果证明了它的经验价值。
英文摘要：
We introduce Reverse Derivative Ascent: a categorical analogue of gradient based methods for machine learning. Our algorithm is defined at the level of so-called reverse differential categories. It can be used to learn the parameters of models which are expressed as morphisms of such categories. Our motivating example is boolean circuits: we show how our algorithm can be applied to such circuits by using the theory of reverse differential categories. Note our methodology allows us to learn the parameters of boolean circuits directly, in contrast to existing binarised neural network approaches. Moreover, we demonstrate its empirical value by giving experimental results on benchmark machine learning datasets.
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