J. Ramsey

Artificial Intelligence · with Glymour · 2006–2019

A researcher in the Science corpus: 9 papers; trusts 33 researchers and is trusted by 44 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 59 citations into it; #4,492 of 35,345 by trust here, #8,525 by citations
• Causal Inference & Discovery — 33 citations into it; #257 of 364 by trust here, #200 by citations
• Probabilistic & Bayesian Methods — 22 citations into it
• Statistics and Probability — 20 citations into it; #3,778 of 12,932 by trust here, #4,650 by citations
• Signal Processing — 17 citations into it; #3,546 of 7,572 by trust here, #2,911 by citations
• Computational Theory and Mathematics — 8 citations into it
• Information Systems — 8 citations into it
• Analytical Chemistry — 7 citations into it; #150 of 1,254 by trust here, #353 by citations

Whom J. Ramsey trusts overall

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

1. David Maxwell Chickering
2. Christopher Meek
3. Tom L. Burr
4. Peter Spirtes
5. Judea Pearl
6. Kun Zhang
7. J. Gerring
8. Richard E. Neapolitan
9. Jiji Zhang
10. David E. Heckerman

Who trusts J. Ramsey overall

1. Jan Peters
2. Jakob Runge
3. Peter Buhlmann
4. Diego Colombo
5. Krzysztof Chalupka
6. Seth R. Flaxman
7. Ruocheng Guo
8. Bryon Aragam
9. Xun Zheng
10. Jiji Zhang

Papers from J. Ramsey's perspective

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

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

Their papers

• Causal Discovery from Heterogeneous/Nonstationary Data (2019) — 316 citations
• Discovering Cyclic Causal Models by Independent Components Analysis (2008) — 208 citations
• A Scalable Conditional Independence Test for Nonlinear, Non-Gaussian Data (2014) — 56 citations
• A million variables and more: the Fast Greedy Equivalence Search algorithm for learning high-dimensional graphical causal models, with an application to functional magnetic resonance images (2016) — 420 citations
• Adjacency-Faithfulness and Conservative Causal Inference (2006) — 301 citations
• Comparison of Strategies for Scalable Causal Discovery of Latent Variable Models from Mixed Data (2018) — 69 citations
• Discovery of Causal Models that Contain Latent Variables through Bayesian Scoring of Independence Constraints (2017) — 36 citations
• Causal Discovery with Linear Non-Gaussian Models under Measurement Error: Structural Identifiability Results (2018) — 27 citations
• Network modelling methods for FMRI (2011) — 2,073 citations