Refined Generalization Analysis of Gradient Descent for Over-parameterized Two-layer Neural Networks with Smooth Activations on Classification Problems

By Atsushi Nitanda, Taiji Suzuki. 2019

16 citations recorded by the source corpus.

Subfields: Statistical Learning Theory

Citations in the corpus

• Generalization Guarantees for Neural Networks via Harnessing the Low-rank Structure of the Jacobian (2019, neutral): “These works build on global convergence results of randomly initialized neural networks [21, 20, 2, 17, 60, 46, 47, 61] which study the gradient descent trajectory via comparisons to a a linearized Neural Tangent Kernel (NTK) learning problem.”