Abstract

We show that the gradient descent algorithm provides an implicit regularization effect in the learning of over-parameterized matrix factorization models and one-hidden-layer neural networks with quadratic activations.

Concretely, we show that given Õ(dr2) random linear measurements of a rank r positive semidefinite matrix X*, we can recover X* by parameterizing it by UU with U ∈ Rd×d and minimizing the squared loss, even if r ≪ d. We prove that starting from a small initialization, gradient descent recovers X* in Õ(√r) iterations approximately. The results solve the conjecture of Gunasekar et al. [16] under the restricted isometry property.

The technique can be applied to analyzing neural networks with one-hidden-layer quadratic activations with some technical modifications.

Algorithmic Regularization in Over-parameterized Matrix Sensing and Neural Networks with Quadratic Activations

To help personalize content, tailor and measure ads and provide a safer experience, we use cookies. By clicking or navigating the site, you agree to allow our collection of information on and off Facebook through cookies. Learn more, including about available controls: Cookie Policy