Johannes Hofmanninger

Computer Vision and Pattern Recognition · 2020

A researcher in the Science corpus: 3 papers; trusts 9 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 22 citations into it
• Artificial Intelligence — 19 citations into it
• Model Architectures — 9 citations into it
• Optimization & Training — 5 citations into it

Whom Johannes Hofmanninger trusts overall

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

1. Kaiming He
2. Diederik P. Kingma
3. Boris Polyak
4. Sergey Ioffe
5. X. Zhang
6. Geoffrey E. Hinton
7. Christian Szegedy
8. Shaoqing Ren
9. Jimmy Ba
10. Alex Krizhevsky

Papers from Johannes Hofmanninger's perspective

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

Their papers

• Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problem (2020) — 570 citations
• Dynamic memory to alleviate catastrophic forgetting in continuous learning settings (2020) — 30 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