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cs.LG2026
Symmetry without a manifold: intrinsic dimension on orbits
Chon-Fai Kam, Miloud Bessafi, Frédéric Cadet
The standard geometric derivation of neural scaling exponents takes the intrinsic dimension of a data manifold as its input. On modular addition in that derivation h…
cs.LG2026
Structured Features Overfit Where Random Features Grok
Chon-Fai Kam, Miloud Bessafi, Frederic Cadet
Xu, Vardi and Safran (ICML 2026) prove that over-parameterized ridge regression over an unstructured random Gaussian feature map groks, with the delay between memorization and gene…
cs.LG2026
Algebraic Representability as the Limiting Regime of Grokking: An Exactly Solvable Model with Holomorphic Activations
Chon-Fai Kam, Xavier Cadet, Miloud Bessafi +1
Neural networks trained on modular arithmetic exhibit grokking, a delayed transition from memorisation to generalisation known to depend on model capacity: too little and the netwo…