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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
ArXiv (abs)PDFHTML

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

5 / 5 papers shown
Title
Understanding In-Context Learning on Structured Manifolds: Bridging Attention to Kernel Methods
Understanding In-Context Learning on Structured Manifolds: Bridging Attention to Kernel Methods
Zhaiming Shen
Alexander Hsu
Rongjie Lai
Wenjing Liao
MLT
140
1
0
12 Jun 2025
SkewRoute: Training-Free LLM Routing for Knowledge Graph Retrieval-Augmented Generation via Score Skewness of Retrieved Context
SkewRoute: Training-Free LLM Routing for Knowledge Graph Retrieval-Augmented Generation via Score Skewness of Retrieved Context
Hairu Wang
Yuan Feng
Yukun Cao
Xike Xie
S. K. Zhou
RALM
15
0
0
28 May 2025
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
75
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
116
1
0
06 May 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
Lei Li
LRM
262
3
0
03 Feb 2025
1