Stefan Hörtling

Probabilistic & Bayesian Methods · 2022

A researcher in the Science corpus: 1 paper; trusts 2 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Probabilistic & Bayesian Methods — 15 citations into it
• Artificial Intelligence — 14 citations into it
• Generative Models — 6 citations into it

Whom Stefan Hörtling trusts overall

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

1. Danilo Jimenez Rezende
2. Charles Blundell
3. Laurent Dinh
4. Rajesh Ranganath
5. Diederik P. Kingma
6. George Papamakarios
7. Balaji Lakshminarayanan
8. Ivan Kobyzev
9. Geoffrey E. Hinton
10. Shakir Mohamed

Papers from Stefan Hörtling's perspective

Ordered by how strongly the researchers Stefan Hörtling 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. Attention is All you Need (Neural Information Processing Systems)
4. Learning Multiple Layers of Features from Tiny Images
5. Gradient-based learning applied to document recognition
6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
7. Long Short-Term Memory
8. GENERATIVE ADVERSARIAL NETS
9. Variational Inference with Normalizing Flows
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Bernstein flows for flexible posteriors in variational Bayes (2022) — 4 citations