Machine learning systems typically apply the same model to both easy and tough cases. This is in stark contrast with humans, who tend to evoke either fast (instinctive) or slow (analytical) thinking depending on the problem difficulty, a property called the dual-process theory of mind. We present FLOWGEN, a graph-generation model inspired by the dual-process theory of mind that generates large graphs incrementally. Depending on the difficulty of completing the graph at the current step, graph generation is routed to either a fast (weaker) or a slow (stronger) model. These modules have identical architectures, but vary in the number of parameters and consequently differ in generative power. Experiments on real-world graphs show that ours can successfully generate graphs similar to those generated by a single large model, while being up to 2x faster.

翻译：机器学习系统通常对容易和困难的情况都采用同样的模型。这与人类形成鲜明对比,人类往往根据问题的困难而快速(诱因)或缓慢(分析)思考(分析),这是一个称为双过程思维理论的属性。我们展示了FLOWGEN,这是一个由双过程思维理论所启发的图形生成模型,它逐渐生成大图。根据在目前阶段完成图表的困难,图形生成被选择为快速(弱)或慢(强)模型。这些模块有相同的结构,但参数数量不同,因此在基因变异能力上也不同。在现实世界的图表上进行的实验显示,我们能够成功地生成与单个大模型所生成的相似的图形,同时速度要快到2x。