95 citations · 201 across the 14 of their papers we have counts for
3 papers · 1 filter
Precision Machine Learning
Eric J. Michaud, Ziming Liu, Max Tegmark
We explore unique considerations involved in fitting ML models to data with very high precision, as is often required for science applications. We empirically compare various funct…
Omnigrok: Grokking Beyond Algorithmic Data
Ziming Liu, Eric J. Michaud, Max Tegmark
Grokking, the unusual phenomenon for algorithmic datasets where generalization happens long after overfitting the training data, has remained elusive. We aim to understand grokking…
Towards Understanding Grokking: An Effective Theory of Representation Learning
Ziming Liu, Ouail Kitouni, Niklas Nolte +3
We aim to understand grokking, a phenomenon where models generalize long after overfitting their training set. We present both a microscopic analysis anchored by an effective theor…