Simple, fast, and flexible framework for matrix completion with infinite width neural networks.

ABSTRACT: Matrix completion problems arise in many applications including recommendation systems, computer vision, and genomics. Increasingly larger neural networks have been successful in many of these applications but at considerable computational costs. Remarkably, taking the width of a neural network to infinity allows for improved computational performance. In this work, we develop an infinite width neural network framework for matrix completion that is simple, fast, and flexible. Simplicity and speed come from the connection between the infinite width limit of neural networks and kernels known as neural tangent kernels (NTK). In particular, we derive the NTK for fully connected and convolutional neural networks for matrix completion. The flexibility stems from a feature prior, which allows enc

SUBMITTER: Radhakrishnan A 

PROVIDER: S-EPMC9169779 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature