46 citations · 145 across the 27 of their papers we have counts for
4 papers · 1 filter
Scaling Spherical CNNs
Carlos Esteves, Jean-Jacques Slotine, Ameesh Makadia
Spherical CNNs generalize CNNs to functions on the sphere, by using spherical convolutions as the main linear operation. The most accurate and efficient way to compute spherical co…
Learning ABCs: Approximate Bijective Correspondence for isolating factors of variation with weak supervision
Kieran A. Murphy, Varun Jampani, Srikumar Ramalingam +1
Representational learning forms the backbone of most deep learning applications, and the value of a learned representation is intimately tied to its information content regarding d…
Time Dependence in Non-Autonomous Neural ODEs
Jared Quincy Davis, Krzysztof Choromanski, Jake Varley +6
Neural Ordinary Differential Equations (ODEs) are elegant reinterpretations of deep networks where continuous time can replace the discrete notion of depth, ODE solvers perform for…
Latent feature disentanglement for 3D meshes
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. In this paper we build upon rec…