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May 29, 2023 · This rate reveals the structural properties of the Transformer and suggests the types of sequential relationships it is best suited for ...
In this work, we investigate the ability of transformers to approximate sequential relationships. We first prove a universal approximation theorem for the ...
This theorem implies that multilayer Transformer networks can approximate mixed and anisotropic smooth functions even if inputs and out- puts are infinite ...
Dec 19, 2019 · We prove that Transformer networks are universal approximators of sequence-to-sequence functions.
Mar 4, 2023 · Abstract. We survey current developments in the approximation theory of sequence modelling in machine learning.
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Sep 5, 2023 · Abstract: In this talk, we present some recent results on the approximation theory of deep learning architectures for sequence modelling.
May 29, 2023 · This rate reveals the structural properties of the Transformer and suggests the types of sequential relationships it is best suited for ...
Feb 27, 2023 · Abstract. We survey current developments in the approximation theory of sequence modelling in ma- chine learning.
Approximation theory of transformer networks for sequence modeling. H Jiang ... Approximation Rate of the Transformer Architecture for Sequence Modeling.
May 29, 2023 · In this work, we investigate the ability of transformers to approximate sequential relationships. We first prove a universal approximation ...