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cs.LG2026
Intrinsic Task Symmetry Drives Generalization in Algorithmic Tasks
Hyeonbin Hwang, Yeachan Park
Grokking, the sudden transition from memorization to generalization, is characterized by the emergence of low-dimensional representations, yet the mechanism underlying this organiz…
cs.LG2024
Absence of Closed-Form Descriptions for Gradient Flow in Two-Layer Narrow Networks
Yeachan Park
In the field of machine learning, comprehending the intricate training dynamics of neural networks poses a significant challenge. This paper explores the training dynamics of neura…
cs.LG2024
Acceleration of Grokking in Learning Arithmetic Operations via Kolmogorov-Arnold Representation
Yeachan Park, Minseok Kim, Yeoneung Kim
We propose novel methodologies aimed at accelerating the grokking phenomenon, which refers to the rapid increment of test accuracy after a long period of overfitting as reported in…