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Found 4 papers, 1 papers with code
An Analysis of SVD for Deep Rotation Estimation
Symmetric orthogonalization via SVD, and closely related procedures, are well-known techniques for projecting matrices onto $O(n)$ or $SO(n)$.
A Random Matrix Perspective on Mixtures of Nonlinearities for Deep Learning
In this work, we focus on this high-dimensional regime in which both the dataset size and the number of features tend to infinity.
A Random Matrix Perspective on Mixtures of Nonlinearities in High Dimensions
One of the distinguishing characteristics of modern deep learning systems is that they typically employ neural network architectures that utilize enormous numbers of parameters, often in the millions and sometimes even in the billions.
Latent feature disentanglement for 3D meshes
Generative modeling of 3D shapes has become an important problem due to its relevance to many applications across Computer Vision, Graphics, and VR.
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