Florian Wenzel

Artificial Intelligence · 2018–2020

A researcher in the Science corpus: 4 papers; trusts 73 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 97 citations into it; #30,584 of 35,345 by trust here, #17,412 by citations
• Probabilistic & Bayesian Methods — 43 citations into it
• Computer Vision and Pattern Recognition — 32 citations into it
• Optimization & Training — 30 citations into it
• Statistics and Probability — 14 citations into it
• Evaluation & Benchmarks — 13 citations into it
• Model Architectures — 13 citations into it
• Computational Theory and Mathematics — 8 citations into it

Whom Florian Wenzel trusts overall

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

1. Alex Krizhevsky
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Yann LeCun
6. Radford M. Neal
7. Kaiming He
8. Jimmy Ba
9. Michael I. Jordan
10. Ruslan Salakhutdinov

Who trusts Florian Wenzel overall

1. Idan Achituve
2. Pavel Izmailov
3. Andrew Gordon Wilson
4. Erik A. Daxberger
5. Haitao Liu
6. Théo Galy-Fajou
7. Matthias Minderer
8. Mitchell Wortsman
9. Jake Snell
10. Aviv Navon

Papers from Florian Wenzel's perspective

Ordered by how strongly the researchers Florian Wenzel 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. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Hyperparameter Ensembles for Robustness and Uncertainty Quantification (2020) — 256 citations
• Appendix for: How Good is the Bayes Posterior in Deep Neural Networks Really? (2020) — 424 citations
• Multi-Class Gaussian Process Classification Made Conjugate: Efficient Inference via Data Augmentation (2019) — 32 citations
• Efficient Gaussian Process Classification Using Polya-Gamma Data Augmentation (2018) — 40 citations