The success of a specific neural network architecture is closely tied to the
dataset and task it tackles; there is no one-size-fits-all solution. Thus,
considerable efforts have been made to quickly and accurately estimate the
performances of neural architectures, without full training or evaluation, for
given tasks and datasets. Neural architecture encoding has played a crucial
role in the estimation, and graphbased methods, which treat an architecture as
a graph, have shown prominent performance. For enhanced representation learning
of neural architectures, we introduce FlowerFormer, a powerful graph
transformer that incorporates the information flows within a neural
architecture. FlowerFormer consists of two key components: (a) bidirectional
asynchronous message passing, inspired by the flows; (b) global attention built
on flow-based masking. Our extensive experiments demonstrate the superiority of
FlowerFormer over existing neural encoding methods, and its effectiveness
extends beyond computer vision models to include graph neural networks and auto
speech recognition models. Our code is available at
this http URL

FlowerFormer 是一个强大的图形变换器，通过结合神经结构内的信息流，进一步加强表示学习，并通过双向异步消息传递和基于流掩码的全局注意力机制，显著优于现有的神经编码方法，扩展应用于计算机视觉模型、图形神经网络和自动语音识别模型。