20 citations · 75 across the 8 of their papers we have counts for
9 papers
Learning Position From Vehicle Vibration Using an Inertial Measurement Unit
Barak Or, Nimrod Segol, Areej Eweida +1
This paper presents a novel approach to vehicle positioning that operates without reliance on the global navigation satellite system (GNSS). Traditional GNSS approaches are vulnera…
Surface Recognition for e-Scooter Using Smartphone IMU Sensor
Areej Eweida, Nimord Segol, Maxim Freydin +2
In recent years, as the use of micromobility gained popularity, technological challenges connected to e-scooters became increasingly important. This paper focuses on road surface r…
Deep Learning for Inertial Sensor Alignment
Maxim Freydin, Niv Sfaradi, Nimrod Segol +2
Accurate alignment of a fixed mobile device equipped with inertial sensors inside a moving vehicle is important for navigation, activity recognition, and other applications. Accura…
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.…
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…
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…
Surface Networks via General Covers
Niv Haim, Nimrod Segol, Heli Ben-Hamu +2
Developing deep learning techniques for geometric data is an active and fruitful research area. This paper tackles the problem of sphere-type surface learning by developing a novel…