论文标题
MPLP：学习消息通过学习协议
MPLP: Learning a Message Passing Learning Protocol
论文作者
论文摘要
我们提出了一种学习人工神经网络的权重的新方法 - 消息传递学习方案（MPLP）。在MPLP中，我们将ANN中发生的每个操作都作为独立代理。每个代理都负责从其他代理中摄入传入的多维消息，更新其内部状态，并生成要传递给相邻代理的多维消息。我们证明了MPLP的生存能力，而不是传统的基于梯度的方法对简单的馈送神经网络的方法，并提出了一个能够概括为非传统神经网络体系结构的框架。 MPLP是使用基于端梯度的元元优化的元学学习的。我们进一步讨论了MPLP观察到的特性，并假设其在深度学习各个领域的适用性。
We present a novel method for learning the weights of an artificial neural network - a Message Passing Learning Protocol (MPLP). In MPLP, we abstract every operations occurring in ANNs as independent agents. Each agent is responsible for ingesting incoming multidimensional messages from other agents, updating its internal state, and generating multidimensional messages to be passed on to neighbouring agents. We demonstrate the viability of MPLP as opposed to traditional gradient-based approaches on simple feed-forward neural networks, and present a framework capable of generalizing to non-traditional neural network architectures. MPLP is meta learned using end-to-end gradient-based meta-optimisation. We further discuss the observed properties of MPLP and hypothesize its applicability on various fields of deep learning.
