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Found 12 papers, 1 papers with code
PLMP -- Point-Line Minimal Problems in Complete Multi-View Visibility
We present a complete classification of all minimal problems for generic arrangements of points and lines completely observed by calibrated perspective cameras.
Changing Views on Curves and Surfaces
Visual events in computer vision are studied from the perspective of algebraic geometry.
Pure and Spurious Critical Points: a Geometric Study of Linear Networks
The critical locus of the loss function of a neural network is determined by the geometry of the functional space and by the parameterization of this space by the network's weights.
PL${}_{1}$P -- Point-line Minimal Problems under Partial Visibility in Three Views
We present a complete classification of minimal problems for generic arrangements of points and lines in space observed partially by three calibrated perspective cameras when each line is incident to at most one point.
PL₁P - Point-line Minimal Problems under Partial Visibility in Three Views
We present a complete classification of minimal problems for generic arrangements of points and lines in space observed partially by three calibrated perspective cameras when each line is incident to at most one point.
Toric invariant theory for maximum likelihood estimation in log-linear models
We establish connections between invariant theory and maximum likelihood estimation for discrete statistical models.
Statistics Theory Algebraic Geometry Statistics Theory 14L24, 14P05, 20G45, 62F10, 62H22, 62R01
The Maximum Likelihood Degree of Linear Spaces of Symmetric Matrices
We study multivariate Gaussian models that are described by linear conditions on the concentration matrix.
Algebraic Geometry Statistics Theory Statistics Theory 62R01, 14C17, 14Q15, 15A15
Geometry of Linear Convolutional Networks
We study the family of functions that are represented by a linear convolutional neural network (LCN).
Snapshot of Algebraic Vision
In this survey article, we present interactions between algebraic geometry and computer vision, which have recently come under the header of algebraic vision.
Function Space and Critical Points of Linear Convolutional Networks
We study the geometry of linear networks with one-dimensional convolutional layers.
Geometry of Linear Neural Networks: Equivariance and Invariance under Permutation Groups
We prove that all invariant linear functions can be parameterized by a single linear autoencoder with a weight-sharing property imposed by the cycle decomposition of the considered permutation.
Order-One Rolling Shutter Cameras
We provide a unified theory for the important class of order-one rolling shutter (RS$_1$) cameras.
