Stephan Bongers

Artificial Intelligence · 2016–2018

A researcher in the Science corpus: 7 papers; trusts 31 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 65 citations into it; #19,986 of 35,345 by trust here, #13,544 by citations
• Causal Inference & Discovery — 54 citations into it; #225 of 364 by trust here, #191 by citations
• Probabilistic & Bayesian Methods — 27 citations into it
• Representation & Self-Supervised Learning — 18 citations into it; #2,919 of 3,145 by trust here, #2,299 by citations
• Statistics and Probability — 17 citations into it; #9,967 of 12,932 by trust here, #5,294 by citations
• Molecular Biology — 16 citations into it
• Computational Theory and Mathematics — 10 citations into it
• Signal Processing — 9 citations into it

Whom Stephan Bongers trusts overall

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

1. Thomas S. Richardson
2. Joris M. Mooij
3. Peter Spirtes
4. Tom L. Burr
5. Kenneth A. Bollen
6. Judea Pearl
7. Peter M. Bentler
8. Jiji Zhang
9. Bernhard Schölkopf
10. Gregory F. Cooper

Who trusts Stephan Bongers overall

1. Jan Peters
2. Bernhard Schölkopf
3. Stefan Bauer
4. Joris M. Mooij
5. Niklas Pfister
6. Sara Magliacane
7. Atticus Geiger
8. Paul K. Rubenstein

Papers from Stephan Bongers's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. Causality : Models , Reasoning , and Inference
4. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
5. Gradient-based learning applied to document recognition
6. Causation, Prediction, and Search
7. Attention is All you Need (Neural Information Processing Systems)
8. GENERATIVE ADVERSARIAL NETS
9. Learning Multiple Layers of Features from Tiny Images
10. Long Short-Term Memory

Their papers

• Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions (2017) — 278 citations
• UvA-DARE ( Digital Academic Repository ) Causal Consistency of Structural Equation Models (2017) — 172 citations
• From Deterministic ODEs to Dynamic Structural Causal Models (2016) — 63 citations
• Foundations of structural causal models with cycles and latent variables (2016) — 235 citations
• From Random Differential Equations to Structural Causal Models: the stochastic case (2018) — 35 citations
• Causal Transfer Learning (2017) — 17 citations
• Theoretical Aspects of Cyclic Structural Causal Models (2016) — 20 citations