Approximation and Estimation Ability of Transformers for Sequence-to-Sequence Functions with Infinite Dimensional Input

30 May 2023

Papers citing "Approximation and Estimation Ability of Transformers for Sequence-to-Sequence Functions with Infinite Dimensional Input"

6 / 6 papers shown

Title
Transformers for Learning on Noisy and Task-Level Manifolds: Approximation and Generalization Insights Zhaiming Shen Alex Havrilla Rongjie Lai A. Cloninger Wenjing Liao 39 0 0 06 May 2025
Approximation Rate of the Transformer Architecture for Sequence Modeling Hao Jiang Qianxiao Li 44 9 0 03 Jan 2025
On Expressive Power of Looped Transformers: Theoretical Analysis and Enhancement via Timestep Encoding Kevin Xu Issei Sato 37 3 0 02 Oct 2024
Are Transformers with One Layer Self-Attention Using Low-Rank Weight Matrices Universal Approximators? T. Kajitsuka Issei Sato 29 16 0 26 Jul 2023
Big Bird: Transformers for Longer Sequences Manzil Zaheer Guru Guruganesh Kumar Avinava Dubey Joshua Ainslie Chris Alberti ... Philip Pham Anirudh Ravula Qifan Wang Li Yang Amr Ahmed VLM 251 2,012 0 28 Jul 2020
ImageNet Large Scale Visual Recognition Challenge Olga Russakovsky Jia Deng Hao Su J. Krause S. Satheesh ... A. Karpathy A. Khosla Michael S. Bernstein Alexander C. Berg Li Fei-Fei VLM ObjD 282 39,190 0 01 Sep 2014