Matthias Sachs

Statistics and Probability · 2019–2020

A researcher in the Science corpus: 2 papers; trusts 21 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Statistics and Probability — 24 citations into it; #11,447 of 12,932 by trust here, #10,855 by citations
• Artificial Intelligence — 21 citations into it; #30,900 of 35,345 by trust here, #32,362 by citations
• Probabilistic & Bayesian Methods — 15 citations into it

Whom Matthias Sachs trusts overall

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

1. Radford M. Neal
2. Gareth O. Roberts
3. Max Welling
4. Matthew J. Beal
5. Nicholas G. Polson
6. Alexandre Bouchard-Cot'e
7. Richard L. Tweedie
8. Sean P. Meyn
9. Stuart Geman
10. Pierre Del Moral

Who trusts Matthias Sachs overall

1. Idan Achituve
2. Christopher Nemeth
3. Aviv Navon
4. Yochai Yemini

Papers from Matthias Sachs's perspective

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

Their papers

• Efficient posterior sampling for high-dimensional imbalanced logistic regression. (2019) — 15 citations
• Posterior Computation with the Gibbs Zig-Zag Sampler (2020) — 7 citations