Carl-Johann Simon-Gabriel

Artificial Intelligence · 2016–2018

A researcher in the Science corpus: 7 papers; trusts 74 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 64 citations into it; #13,266 of 35,345 by trust here, #11,072 by citations
• Computer Vision and Pattern Recognition — 29 citations into it; #12,803 of 21,575 by trust here, #10,334 by citations
• Safety, Robustness & Interpretability — 29 citations into it; #1,357 of 2,251 by trust here, #1,945 by citations
• Statistical Learning Theory — 26 citations into it
• Probabilistic & Bayesian Methods — 21 citations into it
• Generative Models — 19 citations into it; #1,294 of 1,956 by trust here, #949 by citations
• Statistics and Probability — 18 citations into it; #11,636 of 12,932 by trust here, #8,279 by citations
• Optimization & Training — 11 citations into it

Whom Carl-Johann Simon-Gabriel trusts overall

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

1. Arthur Gretton
2. Bharath K. Sriperumbudur
3. Kenji Fukumizu
4. Alex Krizhevsky
5. Bernhard Schölkopf
6. Radford M. Neal
7. Francis R. Bach
8. Alex Smola
9. Geoffrey E. Hinton
10. Ingo Steinwart

Who trusts Carl-Johann Simon-Gabriel overall

1. David Stutz
2. Alberto Bietti
3. Bernhard Schölkopf
4. Matthias Hein
5. Krikamol Muandet
6. Grégoire Mialon
7. Angus Galloway
8. Bernt Schiele
9. Michael Arbel
10. Adam Scibior

Papers from Carl-Johann Simon-Gabriel's perspective

Ordered by how strongly the researchers Carl-Johann Simon-Gabriel 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. Long Short-Term Memory
6. Learning Multiple Layers of Features from Tiny Images
7. ImageNet: A large-scale hierarchical image database
8. Attention is All you Need (Neural Information Processing Systems)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. GENERATIVE ADVERSARIAL NETS

Their papers

• AdaGAN: Boosting Generative Models (2017) — 232 citations
• Adversarial Vulnerability of Neural Networks Increases With Input Dimension (2018) — 49 citations
• Modeling confounding by half-sibling regression (2016) — 76 citations
• Consistent Kernel Mean Estimation for Functions of Random Variables (2016) — 15 citations
• Kernel Distribution Embeddings: Universal Kernels, Characteristic Kernels and Kernel Metrics on Distributions (2016) — 99 citations
• First-Order Adversarial Vulnerability of Neural Networks and Input Dimension (2018) — 110 citations
• From optimal transport to generative modeling: the VEGAN cookbook (2017) — 154 citations