An obstacle to artificial general intelligence is set by the continual learning of multiple tasks of different nature. Recently, various heuristic tricks, both from machine learning and from neuroscience angles, were proposed, but they lack a unified theory ground. Here, we focus on the continual learning in single-layered and multi-layered neural networks of binary weights. A variational Bayesian learning setting is thus proposed, where the neural network is trained in a field-space, rather than the gradient-ill-defined discrete-weight space, and furthermore, the weight uncertainty is naturally incorporated, and modulates the synaptic resources among tasks. From a physics perspective, we translate the variational continual learning into the Franz-Parisi thermodynamic potential framework, where the previous task knowledge acts as a prior and a reference as well. Therefore, the learning performance can be analytically studied with mean-field order parameters, whose predictions coincide with the numerical experiments using stochastic gradient descent methods. Our proposed principled frameworks also connect to elastic weight consolidation, and neuroscience inspired metaplasticity, providing a theory-grounded method for the real-world multi-task learning with deep networks.
翻译:人造一般智能的障碍在于不断学习不同性质的多种任务。最近,提出了从机器学习和神经科学角度进行的各种超自然技巧,但缺乏统一的理论基础。在这里,我们侧重于在单层和多层神经网络中持续学习二进制重量。因此,提出了一种变式贝叶斯学习环境,在这种环境中,神经网络在野外空间而不是梯度定义的离散重量空间中接受培训,此外,重量的不确定性自然地被整合起来,并调节各项任务之间的合成资源。从物理角度看,我们把变异性持续学习转化为弗朗兹-巴黎热动力潜力框架,在这种框架中,先前的任务知识作为先前和参考作用。因此,学习表现可以用中位顺序参数进行分析性研究,这些参数的预测与使用梯度梯度梯度梯度下降方法进行的数字实验相吻合。我们拟议的原则框架也与弹性重量整合和神经科学激励的元性平衡性,为现实世界的多层次学习网络提供理论基础方法。