172 citations · 1.1k across the 55 of their papers we have counts for
7 papers · 1 filter
Secondary Vertex Finding in Jets with Neural Networks
Jonathan Shlomi, Sanmay Ganguly, Eilam Gross +5
Jet classification is an important ingredient in measurements and searches for new physics at particle coliders, and secondary vertex reconstruction is a key intermediate step in b…
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…
SALD: Sign Agnostic Learning with Derivatives
Matan Atzmon, Yaron Lipman
Learning 3D geometry directly from raw data, such as point clouds, triangle soups, or unoriented meshes is still a challenging task that feeds many downstream computer vision and g…
Multiview Neural Surface Reconstruction by Disentangling Geometry and Appearance
Lior Yariv, Yoni Kasten, Dror Moran +4
In this work we address the challenging problem of multiview 3D surface reconstruction. We introduce a neural network architecture that simultaneously learns the unknown geometry,…
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…
Set2Graph: Learning Graphs From Sets
Hadar Serviansky, Nimrod Segol, Jonathan Shlomi +4
Many problems in machine learning can be cast as learning functions from sets to graphs, or more generally to hypergraphs; in short, Set2Graph functions. Examples include clusterin…