This paper is to introduce an asynchronous and local learning framework for neural networks, named Modular Learning Framework (MOLE). This framework modularizes neural networks by layers, defines the training objective via mutual information for each module, and sequentially trains each module by mutual information maximization. MOLE makes the training become local optimization with gradient-isolated across modules, and this scheme is more biologically plausible than BP. We run experiments on vector-, grid- and graph-type data. In particular, this framework is capable of solving both graph- and node-level tasks for graph-type data. Therefore, MOLE has been experimentally proven to be universally applicable to different types of data.

翻译：本文提出了一种名为模块化学习框架（MOLE）的异步局部学习范式，用于神经网络训练。该框架按层级将神经网络模块化，通过互信息为每个模块定义训练目标，并依次通过互信息最大化训练各模块。MOLE使得训练过程成为模块间梯度隔离的局部优化，这种机制比反向传播（BP）更具生物合理性。我们在向量型、网格型和图型数据上进行了实验。特别地，该框架能够解决图型数据的图级任务和节点级任务。因此，MOLE已被实验证明适用于不同数据类型的通用性。