Christian Donner

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 2 papers; trusts 17 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 36 citations into it; #30,632 of 35,345 by trust here, #30,347 by citations
• Probabilistic & Bayesian Methods — 17 citations into it
• Statistics and Probability — 7 citations into it
• Computational Theory and Mathematics — 5 citations into it
• Optimization & Training — 5 citations into it

Whom Christian Donner trusts overall

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

1. James Hensman
2. Michalis K. Titsias
3. David M. Blei
4. Diederik P. Kingma
5. Alexander G. de G. Matthews
6. Matthew D. Hoffman
7. David Arthur
8. Christopher K. I. Williams
9. Michael I. Jordan
10. Radford M. Neal

Who trusts Christian Donner overall

1. Idan Achituve
2. Jake Snell

Papers from Christian Donner's perspective

Ordered by how strongly the researchers Christian Donner trusts endorse each paper. Same corpus, a different order for every researcher.

1. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
2. ImageNet classification with deep convolutional neural networks
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Learning Multiple Layers of Features from Tiny Images
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Attention is All you Need (Neural Information Processing Systems)
9. Auto-Encoding Variational Bayes (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• 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