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J

Joseph M. Landsberg

Researcher at Texas A&M University

Publications -  168
Citations -  5167

Joseph M. Landsberg is an academic researcher from Texas A&M University. The author has contributed to research in topics: Rank (linear algebra) & Matrix multiplication. The author has an hindex of 36, co-authored 162 publications receiving 4754 citations. Previous affiliations of Joseph M. Landsberg include Centre national de la recherche scientifique & University of Pennsylvania.

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An explicit description of the irreducible components of the set of matrix pencils with bounded normal rank

TL;DR: In this article, a new explicit description of each of these irreducible components is provided, which is a parametrization of each component, and one can explicitly construct any pencil in each component.
Book ChapterDOI

Differential Geometry of Submanifolds of Projective Space

TL;DR: In this paper, the rigidity of submanifolds of projective spaces is studied, and the results of [16, 20, 29, 18, 19, 10, 31] are surveyed.
Posted Content

Algebraic geometry and projective differential geometry, Seoul National University concentrated lecture series, 1997

Abstract: This is an expanded and updated version of a lecture series I gave at Seoul National University in September 1997. It is in some sense an update of the 1979 Griffiths and Harris paper with a similar title. I discuss: Homogeneous varieties, Topology and consequences Projective differential invariants, Varieties with degenerate Gauss images, When can a uniruled variety be smooth?, Dual varieties, Linear systems of bounded and constant rank, Secant and tangential varieties, Systems of quadrics with tangential defects, Recognizing uniruled varieties, Recognizing intersections of quadrics, Recognizing homogeneous spaces, Complete intersections. This is a preliminary version, so please send me comments, corrections and questions.
Posted Content

On minimal free resolutions of sub-permanents and other ideals arising in complexity theory

TL;DR: In this article, the linear strand of the minimal free resolution of the ideal generated by k x k sub-permanents of an n x n generic matrix and by square-free monomials of degree k was computed.

Stable recovery of the factors from a deep matrix product

TL;DR: In this article, the Deep Null-Space-Property (DNSP) was proposed to guarantee the stable recovery of the matrix factors in a deep convolutional network, which can be applied to many fast transforms such as FFT.