论文标题
超级编码器：从国家叠加到嘈杂条件下的程序学习
SuperCoder: Program Learning Under Noisy Conditions From Superposition of States
论文作者
论文摘要
我们在特定于域的语言（DSL）中提出了一种新的程序学习方法，该方法基于梯度下降而无需直接搜索。我们方法的第一个组成部分是DSL变量的概率表示。在程序序列中的每个时间步中，在具有一定概率的DSL变量上应用了不同的DSL函数，从而导致不同的结果。我们没有分别处理所有这些输出，它们的数字随着每个时间步的成倍增长而不如将它们收集到变量的叠加中，该变量将信息捕获以单个但模糊状态的状态捕获信息。该状态应通过损失函数在最终时间步长与地面真相输出进行对比。我们方法的第二个组成部分是基于注意力的复发性神经网络，该网络为梯度下降提供了适当的初始化点，以优化概率表示。我们开发的方法超过了合成长程序的最新方法，并且能够在噪音下学习程序。
We propose a new method of program learning in a Domain Specific Language (DSL) which is based on gradient descent with no direct search. The first component of our method is a probabilistic representation of the DSL variables. At each timestep in the program sequence, different DSL functions are applied on the DSL variables with a certain probability, leading to different possible outcomes. Rather than handling all these outputs separately, whose number grows exponentially with each timestep, we collect them into a superposition of variables which captures the information in a single, but fuzzy, state. This state is to be contrasted at the final timestep with the ground-truth output, through a loss function. The second component of our method is an attention-based recurrent neural network, which provides an appropriate initialization point for the gradient descent that optimizes the probabilistic representation. The method we have developed surpasses the state-of-the-art for synthesising long programs and is able to learn programs under noise.
