Patrick Blöbaum

Artificial Intelligence · 2016–2019

A researcher in the Science corpus: 3 papers; trusts 27 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 35 citations into it; #15,633 of 35,345 by trust here, #23,731 by citations
• Causal Inference & Discovery — 22 citations into it; #267 of 364 by trust here, #350 by citations
• Safety, Robustness & Interpretability — 17 citations into it
• Signal Processing — 8 citations into it
• Statistics and Probability — 8 citations into it; #9,509 of 12,932 by trust here, #10,275 by citations
• Probabilistic & Bayesian Methods — 7 citations into it
• Computer Vision and Pattern Recognition — 5 citations into it

Whom Patrick Blöbaum trusts overall

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

1. Dominik Janzing
2. Bernhard Schölkopf
3. Aapo Hyvärinen
4. Jan Peters
5. Kun Zhang
6. Joris M. Mooij
7. Shohei Shimizu
8. Irma Lavagnini
9. Patrik O. Hoyer
10. Eleni Sgouritsa

Who trusts Patrick Blöbaum overall

1. Bernhard Schölkopf
2. Dominik Janzing
3. Ricardo Pio Monti
4. Diviyan Kalainathan
5. Kun Zhang
6. Scott M. Lundberg
7. Ian Covert

Papers from Patrick Blöbaum's perspective

Ordered by how strongly the researchers Patrick Blöbaum 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. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Learning Multiple Layers of Features from Tiny Images
7. GENERATIVE ADVERSARIAL NETS
8. Attention is All you Need (Neural Information Processing Systems)
9. ImageNet Large Scale Visual Recognition Challenge
10. ImageNet: A large-scale hierarchical image database

Their papers

• Error asymmetry in causal and anticausal regression (2016) — 13 citations
• Feature relevance quantification in explainable AI: A causality problem (2019) — 367 citations
• Cause-Effect Inference by Comparing Regression Errors (2018) — 93 citations