Jan-Matthis Lueckmann

Artificial Intelligence · 2017–2020

A researcher in the Science corpus: 3 papers; trusts 24 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 46 citations into it; #10,191 of 35,345 by trust here, #18,586 by citations
• Probabilistic & Bayesian Methods — 32 citations into it; #668 of 1,525 by trust here, #904 by citations
• Statistics and Probability — 25 citations into it; #3,595 of 12,932 by trust here, #4,650 by citations
• Statistical and Nonlinear Physics — 11 citations into it
• Cognitive Neuroscience — 10 citations into it; #5,190 of 10,852 by trust here, #7,600 by citations

Whom Jan-Matthis Lueckmann trusts overall

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

1. Max Welling
2. Radford M. Neal
3. Simon N. Wood
4. Geoffrey E. Hinton
5. Edward Meeds
6. Diederik P. Kingma
7. Alex Graves
8. George Papamakarios
9. Michael U Gutmann
10. MARK A. Beaumont

Who trusts Jan-Matthis Lueckmann overall

1. Joeri Hermans
2. Conor Durkan
3. J. Brehmer
4. George Papamakarios
5. Justin Alsing
6. Gilles Louppe
7. David S. Greenberg
8. Volodimir Begy
9. Charlie Nash
10. Iain Murray

Papers from Jan-Matthis Lueckmann's perspective

Ordered by how strongly the researchers Jan-Matthis Lueckmann 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. Learning Multiple Layers of Features from Tiny Images
8. ImageNet: A large-scale hierarchical image database
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Flexible statistical inference for mechanistic models of neural dynamics (2017) — 334 citations
• Likelihood-free inference with emulator networks (2018) — 155 citations
• sbi: A toolkit for simulation-based inference (2020) — 349 citations