ArticleProceedings of the National Academy of Sciences of the United States of America2022
Simple, fast, and flexible framework for matrix completion with infinite width neural networks.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Image2Reg: Linking chromatin images to gene regulation using genetic and chemical perturbation screens.Cell systems · 2025Article
- Applications of nature-inspired metaheuristic algorithms for tackling optimization problems across disciplines.Scientific reports · 2024Article
- Building a two-way street between cell biology and machine learning.Nature cell biology · 2024Article
- Transfer Learning with Kernel Methods.Nature communications · 2023Article
- Wide and deep neural networks achieve consistency for classification.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
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4 authors.
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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 encoding relationships between coordinates of the target matrix, akin to semisupervised learning. The effectiveness of our framework is demonstrated through competitive results for virtual drug screening and image inpainting/reconstruction. We also provide an implementation in Python to make our framework accessible on standard hardware to a broad audience.
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