We introduce a framework for automatically defining and learning deep generative models with problem-specific structure. We tackle problem domains that are more traditionally solved by algorithms such as sorting, constraint satisfaction for Sudoku, and matrix factorization. Concretely, we train diffusion models with an architecture tailored to the problem specification. This problem specification should contain a graphical model describing relationships between variables, and often benefits from explicit representation of subcomputations. Permutation invariances can also be exploited. Across a diverse set of experiments we improve the scaling relationship between problem dimension and our model's performance, in terms of both training time and final accuracy.
翻译:我们引入了自动定义和学习有特定问题结构的深层基因化模型的框架。 我们处理的问题领域由诸如分类、对数独的制约性满意度和矩阵因子化等算法较为传统地解决。 具体地说, 我们用一个适合问题规格的架构来培训扩散模型。 这个问题的规格应该包含一个图形模型,描述变量之间的关系,并常常受益于子数的清晰表达。 变异性也可以被利用。 在一系列不同的实验中,我们改进了问题维度和模型性能在培训时间和最终准确性两方面的缩放关系。