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Reinhard Heckel

63 papers hereh-index 365.8k citations99 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • sole author4
  • first author24
  • middle author15
  • last author19

Across the 62 of 63 papers where every author was matched, so the position is known.

fields
  • cs.IT18
  • cs.LG17
  • eess.IV9
  • stat.ML8
  • cs.CV4
  • cs.DS2
same name
  • Reinhard Heckel — 7 papers, h 2
  • Reinhard Heckel — 6 papers, h 2
  • Reinhard Heckel — 4 papers, h 3
  • Reinhard Heckel — 2 papers, h 2
  • Reinhard Heckel — 1 paper, h 1

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20112024
most citedDeep Decoder: Concise Image Representations from Untrained Non-convolutional Networks

181 citations · 702 across the 49 of their papers we have counts for

collaborators
Showing 2013Show all

4 papers · 1 filter

stat.ML2013

Compressive Nonparametric Graphical Model Selection For Time Series

Alexander Jung, Reinhard Heckel, Helmut Bölcskei +1

We propose a method for inferring the conditional indepen- dence graph (CIG) of a high-dimensional discrete-time Gaus- sian vector random process from finite-length observations. O…

stat.ML2013★ 10 cited

Robust Subspace Clustering via Thresholding

Reinhard Heckel, Helmut Bölcskei

The problem of clustering noisy and incompletely observed high-dimensional data points into a union of low-dimensional subspaces and a set of outliers is considered. The number of…

cs.IT2013

Noisy Subspace Clustering via Thresholding

Reinhard Heckel, Helmut Bölcskei

We consider the problem of clustering noisy high-dimensional data points into a union of low-dimensional subspaces and a set of outliers. The number of subspaces, their dimensions,…

cs.IT2013

Subspace Clustering via Thresholding and Spectral Clustering

Reinhard Heckel, Helmut Bölcskei

We consider the problem of clustering a set of high-dimensional data points into sets of low-dimensional linear subspaces. The number of subspaces, their dimensions, and their orie…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.