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.

该研究介绍了一种针对神经网络的异步和本地学习框架，名为模块化学习框架（MOLE）。该框架通过分层将神经网络模块化，并通过相互信息定义每个模块的训练目标，通过最大化相互信息顺序训练每个模块。MOLE使训练成为模块之间梯度隔离的局部优化，这种方案比BP更符合生物学可行性。在矢量、网格和图形类型数据上进行了实验。特别是，该框架能够解决图形类型数据的图和节点级任务。因此，经实验证明，MOLE在不同类型的数据上具有普适适用性。

MOLE: 通过最大化互信息的模块化学习框架