Tom Claassen

Artificial Intelligence · 2013–2017

A researcher in the Science corpus: 5 papers; trusts 52 researchers and is trusted by 26 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 63 citations into it; #7,928 of 35,345 by trust here, #12,761 by citations
• Causal Inference & Discovery — 50 citations into it; #194 of 364 by trust here, #249 by citations
• Probabilistic & Bayesian Methods — 22 citations into it
• Representation & Self-Supervised Learning — 20 citations into it; #2,921 of 3,145 by trust here, #2,299 by citations
• Statistics and Probability — 19 citations into it
• Molecular Biology — 17 citations into it
• Information Systems — 9 citations into it
• Computational Theory and Mathematics — 7 citations into it

Whom Tom Claassen trusts overall

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

1. Tom L. Burr
2. Jiji Zhang
3. Peter Spirtes
4. David Maxwell Chickering
5. Judea Pearl
6. Joris M. Mooij
7. Jan Peters
8. Kun Zhang
9. James Cussens
10. Thomas S. Richardson

Who trusts Tom Claassen overall

1. Jan Peters
2. Joris M. Mooij
3. Sara Magliacane
4. Christina Heinze-Deml
5. Fattaneh Jabbari
6. Diego Colombo
7. Marloes H. Maathuis
8. Kari Rantanen
9. Peter Buhlmann
10. Bernhard Schölkopf

Papers from Tom Claassen's perspective

Ordered by how strongly the researchers Tom Claassen 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. Causality : Models , Reasoning , and Inference
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Learning Multiple Layers of Features from Tiny Images
9. GENERATIVE ADVERSARIAL NETS
10. ImageNet: A large-scale hierarchical image database

Their papers

• Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions (2017) — 278 citations
• Learning Sparse Causal Models is not NP-hard (2013) — 132 citations
• Ancestral Causal Inference (2016) — 70 citations
• Causal Transfer Learning (2017) — 17 citations
• Joint Causal Inference from Multiple Contexts (2016) — 300 citations