Diffusion probabilistic models have been successfully used to generate data
from noise. However, most diffusion models are computationally expensive and
difficult to interpret with a lack of theoretical justification. Random feature
models on the other hand have gained popularity due to their interpretability
but their application to complex machine learning tasks remains limited. In
this work, we present a diffusion model-inspired deep random feature model that
is interpretable and gives comparable numerical results to a fully connected
neural network having the same number of trainable parameters. Specifically, we
extend existing results for random features and derive generalization bounds
between the distribution of sampled data and the true distribution using
properties of score matching. We validate our findings by generating samples on
the fashion MNIST dataset and instrumental audio data.

我们提出了一种受扩散模型启发的深度随机特征模型，它具有可解释性，并给出了与具有相同可训练参数数量的全连接神经网络相当的数值结果。我们通过对采样数据分布和真实分布之间的得分匹配性质的属性来推导了随机特征的泛化界限，并通过在时尚 MNIST 数据集和乐器音频数据上生成样本来验证我们的发现。