2 code implementations • NeurIPS 2020 • Jake Levinson, Carlos Esteves, Kefan Chen, Noah Snavely, Angjoo Kanazawa, Afshin Rostamizadeh, Ameesh Makadia
Symmetric orthogonalization via SVD, and closely related procedures, are well-known techniques for projecting matrices onto $O(n)$ or $SO(n)$.
no code implementations • 2 Dec 2019 • Ben Adlam, Jake Levinson, Jeffrey Pennington
In this work, we focus on this high-dimensional regime in which both the dataset size and the number of features tend to infinity.
no code implementations • 25 Sep 2019 • Ben Adlam, Jake Levinson, Jeffrey Pennington
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.
no code implementations • 7 Jun 2019 • Jake Levinson, Avneesh Sud, Ameesh Makadia
Generative modeling of 3D shapes has become an important problem due to its relevance to many applications across Computer Vision, Graphics, and VR.