10 citations · 10 across the 1 of their papers we have counts for
2 papers
cs.LG2020★ 10 cited
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…
cs.LG2020
Implicit Geometric Regularization for Learning Shapes
Amos Gropp, Lior Yariv, Niv Haim +2
Representing shapes as level sets of neural networks has been recently proved to be useful for different shape analysis and reconstruction tasks. So far, such representations were…