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Benign Overfitting and Grokking in ReLU Networks for XOR Cluster Data

Benign Overfitting and Grokking in ReLU Networks for XOR Cluster Data

4 October 2023
Zhiwei Xu
Yutong Wang
Spencer Frei
Gal Vardi
Wei Hu
    MLT
ArXiv (abs)PDFHTML

Papers citing "Benign Overfitting and Grokking in ReLU Networks for XOR Cluster Data"

14 / 14 papers shown
Title
When does compositional structure yield compositional generalization? A kernel theory
When does compositional structure yield compositional generalization? A kernel theory
Samuel Lippl
Kim Stachenfeld
NAICoGe
230
10
0
26 May 2024
From Tempered to Benign Overfitting in ReLU Neural Networks
From Tempered to Benign Overfitting in ReLU Neural Networks
Guy Kornowski
Gilad Yehudai
Ohad Shamir
77
13
0
24 May 2023
A Tale of Two Circuits: Grokking as Competition of Sparse and Dense
  Subnetworks
A Tale of Two Circuits: Grokking as Competition of Sparse and Dense Subnetworks
William Merrill
Nikolaos Tsilivis
Aman Shukla
67
54
0
21 Mar 2023
Unifying Grokking and Double Descent
Unifying Grokking and Double Descent
Peter W. Battaglia
David Raposo
Kelsey
83
32
0
10 Mar 2023
Benign Overfitting in Linear Classifiers and Leaky ReLU Networks from
  KKT Conditions for Margin Maximization
Benign Overfitting in Linear Classifiers and Leaky ReLU Networks from KKT Conditions for Margin Maximization
Spencer Frei
Gal Vardi
Peter L. Bartlett
Nathan Srebro
78
23
0
02 Mar 2023
Grokking modular arithmetic
Grokking modular arithmetic
Andrey Gromov
99
42
0
06 Jan 2023
Omnigrok: Grokking Beyond Algorithmic Data
Omnigrok: Grokking Beyond Algorithmic Data
Ziming Liu
Eric J. Michaud
Max Tegmark
93
84
0
03 Oct 2022
Hidden Progress in Deep Learning: SGD Learns Parities Near the
  Computational Limit
Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit
Boaz Barak
Benjamin L. Edelman
Surbhi Goel
Sham Kakade
Eran Malach
Cyril Zhang
108
133
0
18 Jul 2022
Random Feature Amplification: Feature Learning and Generalization in
  Neural Networks
Random Feature Amplification: Feature Learning and Generalization in Neural Networks
Spencer Frei
Niladri S. Chatterji
Peter L. Bartlett
MLT
92
30
0
15 Feb 2022
A Farewell to the Bias-Variance Tradeoff? An Overview of the Theory of
  Overparameterized Machine Learning
A Farewell to the Bias-Variance Tradeoff? An Overview of the Theory of Overparameterized Machine Learning
Yehuda Dar
Vidya Muthukumar
Richard G. Baraniuk
109
72
0
06 Sep 2021
Fit without fear: remarkable mathematical phenomena of deep learning
  through the prism of interpolation
Fit without fear: remarkable mathematical phenomena of deep learning through the prism of interpolation
M. Belkin
53
186
0
29 May 2021
Finite-sample Analysis of Interpolating Linear Classifiers in the
  Overparameterized Regime
Finite-sample Analysis of Interpolating Linear Classifiers in the Overparameterized Regime
Niladri S. Chatterji
Philip M. Long
86
109
0
25 Apr 2020
Surprises in High-Dimensional Ridgeless Least Squares Interpolation
Surprises in High-Dimensional Ridgeless Least Squares Interpolation
Trevor Hastie
Andrea Montanari
Saharon Rosset
Robert Tibshirani
232
747
0
19 Mar 2019
Regularization Matters: Generalization and Optimization of Neural Nets
  v.s. their Induced Kernel
Regularization Matters: Generalization and Optimization of Neural Nets v.s. their Induced Kernel
Colin Wei
Jason D. Lee
Qiang Liu
Tengyu Ma
252
245
0
12 Oct 2018
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