Georg Langs

Artificial Intelligence · 2014–2020

A researcher in the Science corpus: 9 papers; trusts 150 researchers and is trusted by 29 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 86 citations into it; #8,482 of 35,345 by trust here, #4,602 by citations
• Computer Vision and Pattern Recognition — 76 citations into it
• Generative Models — 21 citations into it
• Safety, Robustness & Interpretability — 17 citations into it
• Model Architectures — 16 citations into it
• Optimization & Training — 12 citations into it
• Representation & Self-Supervised Learning — 11 citations into it
• Medical & Health AI — 6 citations into it

Whom Georg Langs trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Yoshua Bengio
4. Diederik P. Kingma
5. Honglak Lee
6. Yann LeCun
7. Karen Simonyan
8. Kaiming He
9. Ilya Sutskever
10. J. Urgen Schmidhuber

Who trusts Georg Langs overall

1. Giulia Vilone
2. Paul Bergmann
3. Kilian Batzner
4. Vitjan Zavrtanik
5. Wojciech Samek
6. Denis A. Gudovskiy
7. Guansong Pang
8. Jakob Bozic
9. Lukas Ruff
10. Veronika Cheplygina

Papers from Georg Langs's perspective

Ordered by how strongly the researchers Georg Langs 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
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

• Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery (2017) — 2,527 citations
• 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
• Introduction to Radiomics (2020) — 1,534 citations
• Causability and explainability of artificial intelligence in medicine (2019) — 1,566 citations
• f‐AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks (2019) — 1,462 citations
• Situating the default-mode network along a principal gradient of macroscale cortical organization (2016) — 2,174 citations
• Unsupervised Pre-training Across Image Domains Improves Lung Tissue Classification (2014) — 69 citations