May 26, 2022 · In this work, we mathematically analyze the power of RPE-based Transformers regarding whether the model is capable of approximating any ...
In this work, we mathematically analyze the power of RPE-based Transformers regarding whether the model is capable of approximating any continuous sequence-to- ...
This work mathematically analyzes the power of RPE-based Transformers regarding whether the model is capable of approximating any continuous ...
Apr 3, 2024 · In this work, we mathematically analyze the power of RPE-based Transformers regarding whether the model is capable of approximating any ...
Given any fixed input length n, Transformers with APE can approximate any continuous sequence-to-sequence function with arbitrary precision under mild.
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In this work, we mathematically analyze the power of RPE-based Transformers regarding whether the model is capable of approximating any continuous sequence-to- ...
Oct 31, 2022 · Your Transformer May Not be as Powerful as You Expect. (arXiv:2205.13401v2 [cs.LG] UPDATED) https://ift.tt/RUtcXja · 1:43 AM · Oct 31, 2022.
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Dec 1, 2022 · #Neurips2022 Welcome to our poster “Your Transformer May Not be as Powerful as You Expect” at 11 am on Dec 1, in Hall J #219!
Your transformer may not be as powerful as you expect. S Luo, S Li, S Zheng, TY Liu, L Wang, D He. Advances in Neural Information Processing Systems 35, 4301- ...