Title: Implicit Bias of Gradient Descent on Linear Convolutional Networks,
Abstract: We show that gradient descent on full-width linear convolutional networks of
depth $L$ converges to a linear predictor related to the $\ell_{2/L}$ bridge
penalty in the frequency domain. This is in contrast to linearly fully
connected networks, where gradient descent converges to the hard margin linear
support vector machine solution, regardless of depth.