Henning Müller

Computer Vision and Pattern Recognition · Radiology · 2001–2019

A researcher in the Science corpus: 12 papers; trusts 142 researchers and is trusted by 40 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 73 citations into it; #5,396 of 21,575 by trust here, #6,485 by citations
• Artificial Intelligence — 58 citations into it; #21,944 of 35,345 by trust here, #25,809 by citations
• Biomedical Engineering — 37 citations into it; #6,342 of 10,734 by trust here, #4,555 by citations
• Cognitive Neuroscience — 25 citations into it
• Cellular and Molecular Neuroscience — 20 citations into it
• Natural Language Processing — 17 citations into it
• Model Architectures — 13 citations into it
• Safety, Robustness & Interpretability — 12 citations into it

Whom Henning Müller trusts overall

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

1. Geoffrey E. Hinton
2. Kaiming He
3. Yann LeCun
4. Alex Krizhevsky
5. Yoshua Bengio
6. Karen Simonyan
7. Andrew Zisserman
8. Ilya Sutskever
9. Andrea Vedaldi
10. Diederik P. Kingma

Who trusts Henning Müller overall

1. Shiv Ram Dubey
2. Ritendra Datta
3. Thomas Deselaers
4. Guanghai Liu
5. Zhi-Hua Zhou
6. Ying Liu
7. Henning Müller
8. Ronghang Hu
9. Jing-yu Yang
10. Erico Tjoa

Papers from Henning Müller's perspective

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

Their papers

• The IAPR TC-12 Benchmark: A New Evaluation Resource for Visual Information Systems (2006) — 526 citations
• Performance evaluation in content-based image retrieval: overview and proposals (2001) — 658 citations
• Regression Concept Vectors for Bidirectional Explanations in Histopathology (2018) — 89 citations
• Deep Learning with Convolutional Neural Networks Applied to Electromyography Data: A Resource for the Classification of Movements for Prosthetic Hands (2016) — 582 citations
• Why rankings of biomedical image analysis competitions should be interpreted with care (2018) — 393 citations
• Electromyography data for non-invasive naturally-controlled robotic hand prostheses (2014) — 915 citations
• Comparison of six electromyography acquisition setups on hand movement classification tasks (2017) — 414 citations
• VQA-Med: Overview of the Medical Visual Question Answering Task at ImageCLEF 2019 (2019) — 322 citations
• Overview of ImageCLEF 2018 Medical Domain Visual Question Answering Task (2018) — 117 citations
• ChaLearn Joint Contest on Multimedia Challenges Beyond Visual Analysis: An overview (2016) — 89 citations
• The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping. (2016) — 2,940 citations
• ImageCLEF 2014: Overview and Analysis of the Results (2014) — 119 citations