论文标题
朝向模块化算法诱导
Towards Modular Algorithm Induction
论文作者
论文摘要
我们提出了一个模块化神经网络体系结构主，该主管在给定一组输入输出示例的情况下学习算法。主要由一个神经控制器组成，该神经控制器与可变的长度输入磁带相互作用，并学会将模块及其相应的参数选择组成。与以前的方法不同，Main使用通用域形不足的机制来选择模块及其参数。它使用一般的输入胶带布局以及平行的历史记录胶带来指示最近使用的位置。最后，它使用具有长度不变的基于自我注意的输入胶带编码的无内存控制器，以随机访问磁带位置。主要体系结构是使用一组输入输出示例的强化学习端到端训练的。我们在五个算法任务上评估了MAIN，并表明它可以学习与培训相比，其长度更长的输入的政策。
We present a modular neural network architecture Main that learns algorithms given a set of input-output examples. Main consists of a neural controller that interacts with a variable-length input tape and learns to compose modules together with their corresponding argument choices. Unlike previous approaches, Main uses a general domain-agnostic mechanism for selection of modules and their arguments. It uses a general input tape layout together with a parallel history tape to indicate most recently used locations. Finally, it uses a memoryless controller with a length-invariant self-attention based input tape encoding to allow for random access to tape locations. The Main architecture is trained end-to-end using reinforcement learning from a set of input-output examples. We evaluate Main on five algorithmic tasks and show that it can learn policies that generalizes perfectly to inputs of much longer lengths than the ones used for training.
