Beate Sick

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 Beate Sick trusts

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

1. Shakir Mohamed (in Artificial Intelligence)
2. Danilo Jimenez Rezende (in Artificial Intelligence)

Papers from Beate Sick's perspective

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

1. Variational Inference with Normalizing Flows
2. Density estimation using Real NVP
3. Masked Autoregressive Flow for Density Estimation
4. Black Box Variational Inference
5. Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
6. Sylvester Normalizing Flows for Variational Inference
7. Weight Uncertainty in Neural Network
8. Normalizing Flows: An Introduction and Review of Current Methods
9. Attention is All you Need (Neural Information Processing Systems)
10. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)

Their papers

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