Frederick Eberhardt

Artificial Intelligence · 2005–2018

A researcher in the Science corpus: 14 papers; trusts 177 researchers and is trusted by 59 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 78 citations into it; #3,795 of 35,345 by trust here, #5,880 by citations
• Causal Inference & Discovery — 41 citations into it; #52 of 364 by trust here, #51 by citations
• Probabilistic & Bayesian Methods — 40 citations into it
• Statistics and Probability — 26 citations into it; #2,022 of 12,932 by trust here, #2,214 by citations
• Computer Vision and Pattern Recognition — 24 citations into it; #13,155 of 21,575 by trust here, #12,992 by citations
• Optimization & Training — 16 citations into it
• Computational Theory and Mathematics — 14 citations into it
• Molecular Biology — 10 citations into it; #10,369 of 17,773 by trust here, #13,284 by citations

Whom Frederick Eberhardt trusts overall

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

1. David Maxwell Chickering
2. Tom L. Burr
3. Peter Spirtes
4. Judea Pearl
5. Cosma Rohilla Shalizi
6. Christopher Meek
7. Donald B. Rubin
8. Geoffrey E. Hinton
9. Michel X. Goemans
10. Thomas S. Richardson

Who trusts Frederick Eberhardt overall

1. Antti Hyttinen
2. Alain Hauser
3. Bernhard Schölkopf
4. Joris M. Mooij
5. Chandler Squires
6. Peter Buhlmann
7. Jan Peters
8. Yuhao Wang
9. Karren D. Yang
10. Philippe Brouillard

Papers from Frederick Eberhardt's perspective

Ordered by how strongly the researchers Frederick Eberhardt 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet Large Scale Visual Recognition Challenge
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet: A large-scale hierarchical image database
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Visual Causal Feature Learning (2014) — 160 citations
• Interventions and Causal Inference (2007) — 325 citations
• On the Number of Experiments Sufficient and in the Worst Case Necessary to Identify All Causal Relations Among N Variables (2005) — 171 citations
• Estimating Causal Direction and Confounding of Two Discrete Variables (2016) — 8 citations
• Fast Conditional Independence Test for Vector Variables with Large Sample Sizes (2018) — 37 citations
• Causal feature learning: an overview (2017) — 104 citations
• Learning linear cyclic causal models with latent variables (2012) — 150 citations
• Almost Optimal Intervention Sets for Causal Discovery (2008) — 68 citations
• Experiment selection for causal discovery (2013) — 112 citations
• Causal Discovery from Subsampled Time Series Data by Constraint Optimization (2016) — 46 citations
• Multi-Level Cause-Effect Systems (2015) — 64 citations
• Do-calculus when the True Graph Is Unknown (2015) — 66 citations
• Constraint-based Causal Discovery: Conflict Resolution with Answer Set Programming (2014) — 151 citations
• Discovering Cyclic Causal Models with Latent Variables: A General SAT-Based Procedure (2013) — 97 citations