Jochen Garcke

Artificial Intelligence · 2009–2019

A researcher in the Science corpus: 4 papers; trusts 47 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 113 citations into it
• Computer Vision and Pattern Recognition — 55 citations into it
• Statistical and Nonlinear Physics — 32 citations into it
• AI for Physical Sciences — 22 citations into it
• Safety, Robustness & Interpretability — 20 citations into it
• Molecular Biology — 18 citations into it
• Computational Theory and Mathematics — 16 citations into it
• Numerical Methods & Scientific Computing — 13 citations into it

Whom Jochen Garcke trusts overall

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

1. Eric R. Ziegel
2. Tamara G. Kolda
3. Tobias Jahnke
4. Ronald R. Coifman
5. Yann LeCun
6. Kaiming He
7. David Meyer
8. Maizar Raissi
9. Geoffrey E. Hinton
10. Yoshua Bengio

Who trusts Jochen Garcke overall

1. Alireza Doostan
2. Boris N. Khoromskij
3. Noam Wies
4. Yoav Levine
5. Gianluca Iaccarino

Papers from Jochen Garcke's perspective

Ordered by how strongly the researchers Jochen Garcke 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. Gradient-based learning applied to document recognition
4. ImageNet Large Scale Visual Recognition Challenge
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. ImageNet: A large-scale hierarchical image database
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Explainable Machine Learning for Scientific Insights and Discoveries (2019) — 856 citations
• On the numerical solution of the chemical master equation with sums of rank one tensors (2011) — 34 citations
• Informed Machine Learning – A Taxonomy and Survey of Integrating Prior Knowledge into Learning Systems (2019) — 876 citations
• Multivariate Regression and Machine Learning with Sums of Separable Functions (2009) — 127 citations