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Understanding Scaling Laws with Statistical and Approximation Theory for
  Transformer Neural Networks on Intrinsically Low-dimensional Data

Understanding Scaling Laws with Statistical and Approximation Theory for Transformer Neural Networks on Intrinsically Low-dimensional Data

11 November 2024
Alex Havrilla
Wenjing Liao
ArXivPDFHTML

Papers citing "Understanding Scaling Laws with Statistical and Approximation Theory for Transformer Neural Networks on Intrinsically Low-dimensional Data"

6 / 6 papers shown
Title
Learning Guarantee of Reward Modeling Using Deep Neural Networks
Learning Guarantee of Reward Modeling Using Deep Neural Networks
Yuanhang Luo
Yeheng Ge
Ruijian Han
Guohao Shen
21
0
0
10 May 2025
Transformers for Learning on Noisy and Task-Level Manifolds: Approximation and Generalization Insights
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
Transformers Can Overcome the Curse of Dimensionality: A Theoretical Study from an Approximation Perspective
Transformers Can Overcome the Curse of Dimensionality: A Theoretical Study from an Approximation Perspective
Yuling Jiao
Yanming Lai
Yang Wang
Bokai Yan
34
0
0
18 Apr 2025
Approximation Bounds for Transformer Networks with Application to Regression
Approximation Bounds for Transformer Networks with Application to Regression
Yuling Jiao
Yanming Lai
Defeng Sun
Yang Wang
Bokai Yan
29
0
0
16 Apr 2025
Deep Causal Behavioral Policy Learning: Applications to Healthcare
Jonas Knecht
Anna Zink
Jonathan Kolstad
Maya Petersen
CML
83
0
0
05 Mar 2025
Explaining Context Length Scaling and Bounds for Language Models
Explaining Context Length Scaling and Bounds for Language Models
Jingzhe Shi
Qinwei Ma
Hongyi Liu
Hang Zhao
Jeng-Neng Hwang
Serge Belongie
Lei Li
LRM
73
2
0
03 Feb 2025
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