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cs.LG2022★ 1 cited
Towards Antisymmetric Neural Ansatz Separation
Aaron Zweig, Joan Bruna
We study separations between two fundamental models (or \emph{Ansätze}) of antisymmetric functions, that is, functions of the form $f(x_{σ(1)}, \ldots, x_{σ(N)}) = \text{sign}(…
cs.LG2022★ 1 cited
Exponential Separations in Symmetric Neural Networks
Aaron Zweig, Joan Bruna
In this work we demonstrate a novel separation between symmetric neural network architectures. Specifically, we consider the Relational Network~\parencite{santoro2017simple} archit…