Heiko Strathmann

Artificial Intelligence · 2012–2019

A researcher in the Science corpus: 10 papers; trusts 132 researchers and is trusted by 37 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 111 citations into it; #11,095 of 35,345 by trust here, #10,430 by citations
• Statistics and Probability — 89 citations into it; #1,700 of 12,932 by trust here, #2,108 by citations
• Probabilistic & Bayesian Methods — 62 citations into it; #609 of 1,525 by trust here, #710 by citations
• Computer Vision and Pattern Recognition — 42 citations into it; #9,443 of 21,575 by trust here, #8,122 by citations
• Statistical Learning Theory — 29 citations into it
• Generative Models — 28 citations into it
• Representation & Self-Supervised Learning — 18 citations into it
• Optimization & Training — 13 citations into it; #2,506 of 2,601 by trust here, #2,259 by citations

Whom Heiko Strathmann trusts overall

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

1. Radford M. Neal
2. Arthur Gretton
3. Bharath K. Sriperumbudur
4. Christophe Andrieu
5. Diederik P. Kingma
6. Bernhard Schölkopf
7. Alex Smola
8. Kenji Fukumizu
9. Geoffrey E. Hinton
10. Aapo Hyvärinen

Who trusts Heiko Strathmann overall

1. Qiang Liu
2. Arthur Gretton
3. Krikamol Muandet
4. Yingzhen Li
5. Bharath K. Sriperumbudur
6. Jiaxin Shi
7. Yang Song
8. Kenji Fukumizu
9. Yihao Feng
10. Daniel Lévy

Papers from Heiko Strathmann's perspective

Ordered by how strongly the researchers Heiko Strathmann 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. Gradient-based learning applied to document recognition
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. GENERATIVE ADVERSARIAL NETS
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy (2016) — 293 citations
• SOM-VAE: Interpretable Discrete Representation Learning on Time Series (2019) — 155 citations
• Learning deep kernels for exponential family densities (2018) — 79 citations
• Kernel Adaptive Metropolis-Hastings (2013) — 47 citations
• Kernel Sequential Monte Carlo (2015) — 8 citations
• A Kernel Test of Goodness of Fit (2016) — 370 citations
• Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families (2015) — 78 citations
• Deep Self-Organization: Interpretable Discrete Representation Learning on Time Series (2018) — 16 citations
• Kernel Adaptive Metropolis-Hastings (2013)
• Optimal kernel choice for large-scale two-sample tests (2012) — 764 citations