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Found 8 papers, 1 papers with code
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.
Geometry of Linear Convolutional Networks
We study the family of functions that are represented by a linear convolutional neural network (LCN).
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
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.
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.
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.
