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stat.ML2024
Emergence in non-neural models: grokking modular arithmetic via average gradient outer product
Neil Mallinar, Daniel Beaglehole, Libin Zhu +3
Neural networks trained to solve modular arithmetic tasks exhibit grokking, a phenomenon where the test accuracy starts improving long after the model achieves 100% training accura…
stat.ML2024
Feature learning as alignment: a structural property of gradient descent in non-linear neural networks
Daniel Beaglehole, Ioannis Mitliagkas, Atish Agarwala
Understanding the mechanisms through which neural networks extract statistics from input-label pairs through feature learning is one of the most important unsolved problems in supe…