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Found 8 papers, 4 papers with code
Learning Position From Vehicle Vibration Using an Inertial Measurement Unit
This paper presents a novel approach to vehicle positioning that operates without reliance on the global navigation satellite system (GNSS).
Deep Learning for Inertial Sensor Alignment
Accurate alignment of a fixed mobile device equipped with inertial sensors inside a moving vehicle is important for navigation, activity recognition, and other applications.
Improved Convergence Guarantees for Learning Gaussian Mixture Models by EM and Gradient EM
In previous works, the required number of samples had a quadratic dependence on the maximal separation between the K components, and the resulting error estimate increased linearly with this maximal separation.
Secondary Vertex Finding in Jets with Neural Networks
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 building powerful jet classifiers.
High Energy Physics - Experiment High Energy Physics - Phenomenology
Set2Graph: Learning Graphs From Sets
Many problems in machine learning can be cast as learning functions from sets to graphs, or more generally to hypergraphs; in short, Set2Graph functions.
On Universal Equivariant Set Networks
The key theoretical tool used to prove the above results is an explicit characterization of all permutation equivariant polynomial layers.
On the Universality of Invariant Networks
We conclude the paper by proving a necessary condition for the universality of $G$-invariant networks that incorporate only first-order tensors.
Surface Networks via General Covers
Specifically, for the use case of learning spherical signals, our representation provides a low distortion alternative to several popular spherical parameterizations used in deep learning.
