Gabriele Steidl

Computer Vision and Pattern Recognition · 1998–2011

A researcher in the Science corpus: 10 papers; trusts 52 researchers and is trusted by 45 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 58 citations into it; #4,724 of 21,575 by trust here, #8,205 by citations
• Computational Mechanics — 39 citations into it; #1,662 of 10,001 by trust here, #3,652 by citations
• Mathematical Physics — 17 citations into it; #1,802 of 8,562 by trust here, #3,022 by citations
• Applied Mathematics — 16 citations into it; #1,697 of 7,620 by trust here, #1,447 by citations
• Artificial Intelligence — 13 citations into it; #12,184 of 35,345 by trust here, #18,186 by citations
• Numerical Methods & Scientific Computing — 12 citations into it
• Computational Theory and Mathematics — 10 citations into it; #9,960 of 12,182 by trust here, #11,462 by citations
• Radiology, Nuclear Medicine and Imaging — 8 citations into it; #2,707 of 3,265 by trust here, #2,603 by citations

Whom Gabriele Steidl trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Antonin Chambolle
2. Stanley J. Osher
3. Yurii E. Nesterov
4. Patrick L. Combettes
5. Guy Gilboa
6. Amir Beck
7. Tom Goldstein
8. Nir A. Sochen
9. Imre Csiszár
10. Pierre-Louis Lions

Who trusts Gabriele Steidl overall

1. Patrick L. Combettes
2. Laurent Condat
3. Antonin Chambolle
4. Robert Tibshirani
5. Jean-Christophe Pesquet
6. Aaditya Ramdas
7. Veeranjaneyulu Sadhanala
8. Daniel Potts
9. Hartmut Fuhr
10. Fabrizio Argenti

Papers from Gabriele Steidl's perspective

Ordered by how strongly the researchers Gabriele Steidl trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Regression Shrinkage and Selection via the Lasso
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Gradient-based learning applied to document recognition
6. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
7. ImageNet: A large-scale hierarchical image database
8. Long Short-Term Memory
9. Attention is All you Need (Neural Information Processing Systems)
10. Nonlinear total variation based noise removal algorithms

Their papers

• Combined SVM-Based Feature Selection and Classification (2005) — 279 citations
• Infimal convolution regularizations with discrete ℓ1-type functionals (2011) — 118 citations
• Shearlet Coorbit Spaces: Compactly Supported Analyzing Shearlets, Traces and Embeddings (2011) — 116 citations
• Removing Multiplicative Noise by Douglas-Rachford Splitting Methods (2010) — 295 citations
• Deblurring Poissonian images by split Bregman techniques (2010) — 353 citations
• Shearlet coorbit spaces and associated Banach frames (2009) — 177 citations
• Generalized coorbit theory, Banach frames, and the relation to α‐modulation spaces (2008) — 78 citations
• Splines in Higher Order TV Regularization (2006) — 88 citations
• Fast Fourier Transforms for Nonequispaced Data: A Tutorial (2001) — 351 citations
• Fast and stable algorithms for discrete spherical Fourier transforms (1998)