Explore how Fourier transforms on graphs power spectral GCNs, from modeling heat flow to solving cold-start recommendations.

A review of the mathematical essentials for recommender systems, comparing matrix factorization and graph neural networks.

In this post, we will explore the Low Rank Approximation (LoRA) technique for shrinking neural networks for embedded systems. We will focus on the Convolutional Neural Network (CNN) case and discuss the rank selection process.

In this post, we will explore the Low Rank Approximation (LoRA) technique for shrinking neural networks for embedded systems. We will focus on the Convolutional Neural Network (CNN) case and discuss the rank selection process.

An elementary explanation of the problem with full rank matrices in neural networks and their solution via low rank approximations. Detailed explanation on how to set up the optimization problem and how to solve it, in possibly linear time.