May 30, 2023 · Abstract:We prove an inverse approximation theorem for the approximation of nonlinear sequence-to-sequence relationships using recurrent ...
Jan 16, 2024 · The paper introduces an inverse approximation theorem for non-linear recurrent neural networks, extending known, results for the linear case.
Aug 22, 2023 · Abstract. We prove an inverse approximation theorem for the approximation of nonlinear sequence-to-sequence relationships using RNNs.
Feb 6, 2024 · We prove an inverse approximation theorem for the approximation of nonlinear sequence-to-sequence relationships using recurrent neural ...
We study the approximation properties and optimization dynamics of recurrent neural networks (RNNs) when applied to learn input-output relationships in temporal ...
May 30, 2023 · Abstract. We prove an inverse approximation theorem for the approximation of nonlinear sequence-to-sequence relationships using recurrent neural ...
Inverse approximation theory for nonlinear recurrent neural networks. S Wang, Z Li, Q Li. International Conference on Learning Representations (ICLR) (Spotlight) ...
Although recurrent models benefit from low inference costs, this curse restricts their effectiveness for tasks involving long sequences. In this paper, we study ...
We perform a systematic study of the approximation properties and optimization dynamics of recurrent neural networks (RNNs) when applied to learn ...
We prove an inverse approximation theorem for the approximation of nonlinear sequence-to-sequence relationships using recurrent neural networks (RNNs). 12.