Carola-Bibiane Schönlieb

Computer Vision and Pattern Recognition · 2012–2021

A researcher in the Science corpus: 8 papers; trusts 277 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 187 citations into it; #12,284 of 21,575 by trust here, #12,689 by citations
• Computational Mechanics — 101 citations into it; #7,526 of 10,001 by trust here, #6,877 by citations
• Artificial Intelligence — 96 citations into it
• Radiology, Nuclear Medicine and Imaging — 79 citations into it; #2,752 of 3,265 by trust here, #2,073 by citations
• Biomedical Engineering — 46 citations into it
• Mathematical Physics — 46 citations into it; #7,121 of 8,562 by trust here, #5,408 by citations
• Media Technology — 39 citations into it; #2,047 of 3,621 by trust here, #2,280 by citations
• Statistics and Probability — 37 citations into it

Whom Carola-Bibiane Schönlieb trusts overall

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

1. Antonin Chambolle
2. Kristian Bredies
3. Thomas Pock
4. Karl Kunisch
5. Geoffrey E. Hinton
6. Yann LeCun
7. Yoshua Bengio
8. Kaiming He
9. Alex Krizhevsky
10. Luminita A. Vese

Who trusts Carola-Bibiane Schönlieb overall

1. Martin Benning
2. Fabian Pedregosa
3. Peter Ochs
4. Martin Burger

Papers from Carola-Bibiane Schönlieb's perspective

Ordered by how strongly the researchers Carola-Bibiane Schönlieb 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. ImageNet: A large-scale hierarchical image database
5. Gradient-based learning applied to document recognition
6. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
7. Attention is All you Need (Neural Information Processing Systems)
8. Long Short-Term Memory
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Bilevel approaches for learning of variational imaging models (2015) — 100 citations
• Unified Focal loss: Generalising Dice and cross entropy-based losses to handle class imbalanced medical image segmentation (2021) — 662 citations
• Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans (2020) — 936 citations
• Solving inverse problems using data-driven models (2019) — 728 citations
• On the Connection Between Adversarial Robustness and Saliency Map Interpretability (2019) — 181 citations
• Learning to Diversify Deep Belief Networks for Hyperspectral Image Classification (2017) — 321 citations
• The structure of optimal parameters for image restoration problems (2015) — 58 citations
• A Combined First and Second Order Variational Approach for Image Reconstruction (2012) — 316 citations