14 citations · 64 across the 12 of their papers we have counts for
24 papers
Augmenting Implicit Neural Shape Representations with Explicit Deformation Fields
Matan Atzmon, David Novotny, Andrea Vedaldi +1
Implicit neural representation is a recent approach to learn shape collections as zero level-sets of neural networks, where each shape is represented by a latent code. So far, the…
Moser Flow: Divergence-based Generative Modeling on Manifolds
Noam Rozen, Aditya Grover, Maximilian Nickel +1
We are interested in learning generative models for complex geometries described via manifolds, such as spheres, tori, and other implicit surfaces. Current extensions of existing (…
Riemannian Convex Potential Maps
Samuel Cohen, Brandon Amos, Yaron Lipman
Modeling distributions on Riemannian manifolds is a crucial component in understanding non-Euclidean data that arises, e.g., in physics and geology. The budding approaches in this…
Phase Transitions, Distance Functions, and Implicit Neural Representations
Yaron Lipman
Representing surfaces as zero level sets of neural networks recently emerged as a powerful modeling paradigm, named Implicit Neural Representations (INRs), serving numerous downstr…
Isometric Autoencoders
Amos Gropp, Matan Atzmon, Yaron Lipman
High dimensional data is often assumed to be concentrated on or near a low-dimensional manifold. Autoencoders (AE) is a popular technique to learn representations of such data by p…
Global Attention Improves Graph Networks Generalization
Omri Puny, Heli Ben-Hamu, Yaron Lipman
This paper advocates incorporating a Low-Rank Global Attention (LRGA) module, a computation and memory efficient variant of the dot-product attention (Vaswani et al., 2017), to Gra…