We show that the low-rank approximation of NFKs derived from unsupervised generative models and supervised learning models gives rise to high-quality compact representations of data, achieving competitive results on a variety of machine learning tasks.
Deep linear networks trained with gradient descent yield low rank solutions, as is typically studied in matrix factorization.
We analyze the learning dynamics of infinitely wide neural networks with a finite sized bottle-neck.
The Hessian of neural networks can be decomposed into a sum of two matrices: (i) the positive semidefinite generalized Gauss-Newton matrix G, and (ii) the matrix H containing negative eigenvalues.
Deep Residual Networks present a premium in performance in comparison to conventional networks of the same depth and are trainable at extreme depths.
In this paper, we introduce a spherical embedding technique to position a given set of silhouettes of an object as observed from a set of cameras arbitrarily positioned around the object.