Thijs van Ommen

Artificial Intelligence · 2014–2017

A researcher in the Science corpus: 3 papers; trusts 35 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 47 citations into it; #19,983 of 35,345 by trust here, #15,866 by citations
• Statistics and Probability — 25 citations into it; #8,250 of 12,932 by trust here, #7,508 by citations
• Causal Inference & Discovery — 24 citations into it
• Representation & Self-Supervised Learning — 18 citations into it; #2,918 of 3,145 by trust here, #2,299 by citations
• Probabilistic & Bayesian Methods — 16 citations into it; #1,148 of 1,525 by trust here, #1,198 by citations
• Statistical Learning Theory — 8 citations into it
• Evaluation & Benchmarks — 7 citations into it
• Information Systems — 5 citations into it

Whom Thijs van Ommen trusts overall

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

1. Peter D. Grünwald
2. Jorma Rissanen
3. Tong Zhang
4. Kun Zhang
5. Tom L. Burr
6. Judea Pearl
7. Bernhard Schölkopf
8. Joris M. Mooij
9. A. P. Dawid
10. Elias Bareinboim

Who trusts Thijs van Ommen overall

1. Bernhard Schölkopf
2. Andrew Gordon Wilson
3. Pavel Izmailov
4. Divyat Mahajan
5. Christina Heinze-Deml

Papers from Thijs van Ommen's perspective

Ordered by how strongly the researchers Thijs van Ommen 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. Learning Multiple Layers of Features from Tiny Images
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet: A large-scale hierarchical image database
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions (2017) — 278 citations
• Inconsistency of Bayesian Inference for Misspecified Linear Models, and a Proposal for Repairing It (2014) — 312 citations
• Causal Transfer Learning (2017) — 17 citations