Karin Schnass

Computational Mechanics · 2007–2013

A researcher in the Science corpus: 2 papers; trusts 31 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Computational Mechanics — 28 citations into it; #3,244 of 10,001 by trust here, #3,908 by citations
• Signal Processing — 18 citations into it; #2,487 of 7,572 by trust here, #3,099 by citations
• Computer Vision and Pattern Recognition — 13 citations into it; #11,799 of 21,575 by trust here, #20,271 by citations
• Applied Mathematics — 8 citations into it
• Statistical Learning Theory — 5 citations into it; #725 of 1,038 by trust here, #985 by citations
• Optimization & Training — 5 citations into it

Whom Karin Schnass trusts overall

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

1. Joel A. Tropp
2. E.J. Candes
3. David L. Donoho
4. Alekh Agarwal
5. Sanjeev Arora
6. Ingrid Daubechies
7. Michel Ledoux
8. Julien Mairal
9. Roman Vershynin
10. Gitta Kutyniok

Who trusts Karin Schnass overall

1. Alekh Agarwal
2. Anima Anandkumar
3. Julio Martin Duarte-Carvajalino
4. Prateek Jain
5. Shuheng Zhou
6. Praneeth Netrapalli
7. Jun Won Choi
8. John Wright
9. Zeyuan Allen-Zhu

Papers from Karin Schnass's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
6. ImageNet: A large-scale hierarchical image database
7. Learning Multiple Layers of Features from Tiny Images
8. Attention is All you Need (Neural Information Processing Systems)
9. Long Short-Term Memory
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Compressed Sensing and Redundant Dictionaries (2007) — 577 citations
• On the Identifiability of Overcomplete Dictionaries via the Minimisation Principle Underlying K-SVD (2013) — 66 citations