Task: Language Identification
Input (Spanish, English): This paper studies neural models that can be applied to set-structured inputs and thus require permutation invariance or equivariance. After a first section that introduces necessary and sufficient conditions for permutation invariance/equivariance, the authors present experiments in supervised and semi-supervised learning on point-cloud data as well as cosmology data.    The reviewers agreed that this is a very promising line of work and acknowledged the effort of the authors to improve their paper after the initial discussion phase. However, they also agree that the work appears to be missing more convincing numerical experiments and insights on the choice of neural architectures in the class of permutation-covariant.     In light of these reviews, the AC invites their work to the workshop track.   Also, I would like to emphasize an aspect of this work that I think should be addressed in the subsequent revision.    As the authors rightfully show (thm 2.1), permutation equivariance puts very strong constraints in the class of 1-layer networks. This theorem, while rigorous, reflects a simple algebraic property of matrices that commute with permutation matrices. It is therefore not very surprising, and the resulting architecture relatively obvious. So much so that it already exists in the literature. In fact, it is a particular instance of the graph neural network model of Scarselli et al. '09 (
Output (English): en