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no code implementations • ECCV 2020 • Timothy Duff, Kathlén Kohn, Anton Leykin, Tomas Pajdla

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.

no code implementations • 3 Aug 2021 • Kathlén Kohn, Thomas Merkh, Guido Montúfar, Matthew Trager

We study the family of functions that are represented by a linear convolutional neural network (LCN).

no code implementations • 14 Dec 2020 • Carlos Améndola, Kathlén Kohn, Philipp Reichenbach, Anna Seigal

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

no code implementations • 1 Dec 2020 • Carlos Améndola, Lukas Gustafsson, Kathlén Kohn, Orlando Marigliano, Anna Seigal

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

no code implementations • 10 Mar 2020 • Timothy Duff, Kathlén Kohn, Anton Leykin, Tomas Pajdla

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.

no code implementations • ICLR 2020 • Matthew Trager, Kathlén Kohn, Joan Bruna

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.

1 code implementation • 24 Mar 2019 • Timothy Duff, Kathlén Kohn, Anton Leykin, Tomas Pajdla

We present a complete classification of all minimal problems for generic arrangements of points and lines completely observed by calibrated perspective cameras.

no code implementations • 6 Jul 2017 • Kathlén Kohn, Bernd Sturmfels, Matthew Trager

Visual events in computer vision are studied from the perspective of algebraic geometry.

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