This paper presents an empirical study on the weights of neural networks,
where we interpret each model as a point in a high-dimensional space -- the
neural weight space. To explore the complex structure of this space, we sample
from a diverse selection of training variations (dataset, optimization
procedure, architecture, etc.) of neural network classifiers, and train a large
number of models to represent the weight space. Then, we use a machine learning
approach for analyzing and extracting information from this space. Most
centrally, we train a number of novel deep meta-classifiers with the objective
of classifying different properties of the training setup by identifying their
footprints in the weight space. Thus, the meta-classifiers probe for patterns
induced by hyper-parameters, so that we can quantify how much, where, and when
these are encoded through the optimization process. This provides a novel and
complementary view for explainable AI, and we show how meta-classifiers can
reveal a great deal of information about the training setup and optimization,
by only considering a small subset of randomly selected consecutive weights. To
promote further research on the weight space, we release the neural weight
space (NWS) dataset -- a collection of 320K weight snapshots from 16K
individually trained deep neural networks.

本文介绍了一个关于神经网络权重的实证研究。通过采样多种不同神经网络分类器的训练变化，使用机器学习方法从权重空间中提取和分析信息，特别地构建了一些新型的深度元分类器，用于检测通过超参识别并在优化过程中编码为权重的模式，从而提供了一种新颖和互补的可解释 AI 视角，并释放了一个神经权重空间（NWS）数据集，以促进进一步研究。