Andreas Munk

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 3 papers; trusts 13 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 69 citations into it
• Probabilistic & Bayesian Methods — 62 citations into it
• Statistics and Probability — 21 citations into it
• Nuclear and High Energy Physics — 19 citations into it

Whom Andreas Munk trusts overall

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

1. Frank D. Wood
2. Jan-Willem van de Meent
3. Radford M. Neal
4. Sepp Hochreiter
5. Diederik P. Kingma
6. Tuan Anh Le
7. Geoffrey E. Hinton
8. J. Urgen Schmidhuber
9. Atilim Gunes Baydin
10. Yann LeCun

Papers from Andreas Munk's perspective

Ordered by how strongly the researchers Andreas Munk 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. Gradient-based learning applied to document recognition
4. Long Short-Term Memory
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Auto-Encoding Variational Bayes (International Conference on Learning Representations)
7. Attention is All you Need (Neural Information Processing Systems)
8. ImageNet: A large-scale hierarchical image database
9. Learning Multiple Layers of Features from Tiny Images
10. Et al

Their papers

• Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model (2018) — 33 citations
• Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale (2019) — 63 citations
• Deep Probabilistic Surrogate Networks for Universal Simulator Approximation (2019) — 15 citations