← Search

Dave Helmbold

2 accepted papers

2018

Gradient descent with identity initialization efficiently learns positive definite linear transformations by deep residual networks

ICML 2018oral

We analyze algorithms for approximating a function $f(x) = \Phi x$ mapping $\Re^d$ to $\Re^d$ using deep linear neural networks, i.e. that learn a function $h$ parameterized by matrices $\Theta_1,...,\Theta_L$ and defined by $h(x) = \Theta_L \Theta_{L-1} ... \Theta_1 x$. We focus on algorithms that…

Cited by 158SourcePDFScholar