3 accepted papers
Several machine learning models are defined for inputs of any size, such as graphs with different numbers of nodes and point clouds with varying numbers of points. The universality properties of such any-dimensional models remain poorly understood, as universality is traditionally studied for models…
Many modern learning tasks require models that can take inputs of varying sizes. Consequently, dimension-independent architectures have been proposed for domains where the inputs are graphs, sets, and point clouds. Recent work on graph neural networks has explored whether a model trained on low-dime…
Traditional supervised learning aims to learn an unknown mapping by fitting a function to a set of input-output pairs with a fixed dimension. The fitted function is then defined on inputs of the same dimension. However, in many settings, the unknown mapping takes inputs in any dimension; examples in…