Diffusion probabilistic models have quickly become a major approach for
generative modeling of images, 3D geometry, video and other domains. However,
to adapt diffusion generative modeling to these domains the denoising network
needs to be carefully designed for each domain independently, oftentimes under
the assumption that data lives in a Euclidean grid. In this paper we introduce
Diffusion Probabilistic Fields (DPF), a diffusion model that can learn
distributions over continuous functions defined over metric spaces, commonly
known as fields. We extend the formulation of diffusion probabilistic models to
deal with this field parametrization in an explicit way, enabling us to define
an end-to-end learning algorithm that side-steps the requirement of
representing fields with latent vectors as in previous approaches (Dupont et
al., 2022a; Du et al., 2021). We empirically show that, while using the same
denoising network, DPF effectively deals with different modalities like 2D
images and 3D geometry, in addition to modeling distributions over fields
defined on non-Euclidean metric spaces.

本文介绍了一种新的扩展 Diffusion probabilistic models 方法的方法 Diffusion Probabilistic Fields (DPF), 可用于在度量空间中学习定义为连续函数的概率分布，通过将场参数化作为一种显式形式来解决以前方法的问题。与以前的方法相比，DPF 在处理 2D 图像、3D 几何等不同模态以及定义在非欧几里德度量空间上的领域上的分布时表现出色。

扩散概率场

Diffusion Probabilistic Fields

Graph Neural Networks (GNNs) are the first choice for learning algorithms on
graph data. GNNs promise to integrate (i) node features as well as (ii) edge
information in an end-to-end learning algorithm. How does this promise work out
practically? In this paper, we study to what extend GNNs are necessary to solve
prominent graph classification problems. We find that for graph classification,
a GNN is not more than the sum of its parts. We also find that, unlike
features, predictions with an edge-only model do not always transfer to GNNs.

本文研究了图形分类问题中图神经网络是否必要，发现对于图分类来说，GNN 不仅仅是其部分之和。同时，我们还发现，与特征不同，仅使用边进行预测的模型并不总是能够转换为 GNN。