Any-Dimensional Learning by Sampling Handles Variable-Size Inputs

arXiv·low signal

This work addresses models that must accept inputs of different sizes — such as point clouds with varying numbers of points or variable-length sequences — via a sampling-based any-dimensional learning formulation. It is primarily a theoretical contribution (math.ST) with potential downstream relevance to flexible-input architectures.