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2411.06646
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Understanding Scaling Laws with Statistical and Approximation Theory for Transformer Neural Networks on Intrinsically Low-dimensional Data
11 November 2024
Alex Havrilla
Wenjing Liao
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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
Yuanhang Luo
Yeheng Ge
Ruijian Han
Guohao Shen
19
0
0
10 May 2025
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
Yuling Jiao
Yanming Lai
Yang Wang
Bokai Yan
34
0
0
18 Apr 2025
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
81
0
0
05 Mar 2025
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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