14 citations · 39 across the 5 of their papers we have counts for
4 papers · 1 filter
Improved Convergence Guarantees for Learning Gaussian Mixture Models by EM and Gradient EM
Nimrod Segol, Boaz Nadler
We consider the problem of estimating the parameters a Gaussian Mixture Model with K components of known weights, all with an identity covariance matrix. We make two contributions.…
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…
On Universal Equivariant Set Networks
Nimrod Segol, Yaron Lipman
Using deep neural networks that are either invariant or equivariant to permutations in order to learn functions on unordered sets has become prevalent. The most popular, basic mode…
On the Universality of Invariant Networks
Haggai Maron, Ethan Fetaya, Nimrod Segol +1
Constraining linear layers in neural networks to respect symmetry transformations from a group is a common design principle for invariant networks that has found many applicati…