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- W4281569331 abstract "Explicit antisymmetrization of a neural network is a potential candidate for a universal function approximator for generic antisymmetric functions, which are ubiquitous in quantum physics. However, this procedure is a priori factorially costly to implement, making it impractical for large numbers of particles. The strategy also suffers from a sign problem. Namely, due to near-exact cancellation of positive and negative contributions, the magnitude of the antisymmetrized function may be significantly smaller than before anti-symmetrization. We show that the anti-symmetric projection of a two-layer neural network can be evaluated efficiently, opening the door to using a generic antisymmetric layer as a building block in anti-symmetric neural network Ansatzes. This approximation is effective when the sign problem is controlled, and we show that this property depends crucially the choice of activation function under standard Xavier/He initialization methods. As a consequence, using a smooth activation function requires re-scaling of the neural network weights compared to standard initializations." @default.
- W4281569331 created "2022-05-27" @default.
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- W4281569331 date "2022-05-24" @default.
- W4281569331 modified "2023-09-27" @default.
- W4281569331 title "Efficient anti-symmetrization of a neural network layer by taming the sign problem" @default.
- W4281569331 doi "https://doi.org/10.48550/arxiv.2205.12250" @default.
- W4281569331 hasPublicationYear "2022" @default.
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