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Grokking Explained: A Statistical Phenomenon

Grokking Explained: A Statistical Phenomenon

3 February 2025
B. W. Carvalho
Artur Garcez
Luís C. Lamb
Emílio Vital Brazil
ArXiv (abs)PDFHTMLGithub

Papers citing "Grokking Explained: A Statistical Phenomenon"

10 / 10 papers shown
Grokking in Linear Estimators -- A Solvable Model that Groks without
  Understanding
Grokking in Linear Estimators -- A Solvable Model that Groks without Understanding
Noam Levi
Alon Beck
Yohai Bar-Sinai
189
23
0
25 Oct 2023
Grokking as the Transition from Lazy to Rich Training Dynamics
Grokking as the Transition from Lazy to Rich Training DynamicsInternational Conference on Learning Representations (ICLR), 2023
Tanishq Kumar
Blake Bordelon
Samuel Gershman
Cengiz Pehlevan
413
80
0
09 Oct 2023
Are Emergent Abilities of Large Language Models a Mirage?
Are Emergent Abilities of Large Language Models a Mirage?Neural Information Processing Systems (NeurIPS), 2023
Rylan Schaeffer
Alycia Lee
Oluwasanmi Koyejo
LRM
519
610
0
28 Apr 2023
Progress measures for grokking via mechanistic interpretability
Progress measures for grokking via mechanistic interpretabilityInternational Conference on Learning Representations (ICLR), 2023
Neel Nanda
Lawrence Chan
Tom Lieberum
Jess Smith
Jacob Steinhardt
643
728
0
12 Jan 2023
Grokking phase transitions in learning local rules with gradient descent
Grokking phase transitions in learning local rules with gradient descentJournal of machine learning research (JMLR), 2022
Bojan Žunkovič
E. Ilievski
346
26
0
26 Oct 2022
Towards Understanding Grokking: An Effective Theory of Representation
  Learning
Towards Understanding Grokking: An Effective Theory of Representation LearningNeural Information Processing Systems (NeurIPS), 2022
Ziming Liu
O. Kitouni
Niklas Nolte
Eric J. Michaud
Max Tegmark
Mike Williams
AI4CE
392
230
0
20 May 2022
Grokking: Generalization Beyond Overfitting on Small Algorithmic
  Datasets
Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets
Alethea Power
Yuri Burda
Harrison Edwards
Igor Babuschkin
Vedant Misra
479
560
0
06 Jan 2022
Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges
Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges
M. Bronstein
Joan Bruna
Taco S. Cohen
Petar Velivcković
GNN
1.2K
1,489
0
27 Apr 2021
Failing Loudly: An Empirical Study of Methods for Detecting Dataset
  Shift
Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift
Stephan Rabanser
Stephan Günnemann
Zachary Chase Lipton
397
434
0
29 Oct 2018
Microsoft COCO: Common Objects in Context
Microsoft COCO: Common Objects in ContextEuropean Conference on Computer Vision (ECCV), 2014
Nayeon Lee
Michael Maire
Serge J. Belongie
Lubomir Bourdev
Ross B. Girshick
James Hays
Pietro Perona
Deva Ramanan
C. L. Zitnick
Piotr Dollár
ObjD
27.0K
51,414
0
01 May 2014
1
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