Ruben Sanchez-Romero
Artificial Intelligence · 2016–2019
A researcher in the Science corpus: 3 papers; trusts 13 researchers and is trusted by 5 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 23 citations into it; #21,769 of 35,345 by trust here, #16,471 by citations
- Causal Inference & Discovery — 15 citations into it; #239 of 364 by trust here, #164 by citations
- Probabilistic & Bayesian Methods — 12 citations into it
- Statistics and Probability — 10 citations into it
- Signal Processing — 6 citations into it
- Computational Theory and Mathematics — 5 citations into it; #11,986 of 12,182 by trust here, #5,724 by citations
Whom Ruben Sanchez-Romero trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kun Zhang
- David Maxwell Chickering
- Christopher Meek
- Judea Pearl
- Bernhard Schölkopf
- Biwei Huang
- David Ruppert
- Tom L. Burr
- Jan Peters
- Arthur Gretton
Who trusts Ruben Sanchez-Romero overall
Papers from Ruben Sanchez-Romero's perspective
Ordered by how strongly the researchers Ruben Sanchez-Romero trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Causality : Models , Reasoning , and Inference
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Learning Multiple Layers of Features from Tiny Images
- Attention is All you Need (Neural Information Processing Systems)
- GENERATIVE ADVERSARIAL NETS
- Long Short-Term Memory
- Optimal Structure Identification With Greedy Search
Their papers
- Causal Discovery from Heterogeneous/Nonstationary Data (2019) — 316 citations
- Behind Distribution Shift: Mining Driving Forces of Changes and Causal Arrows (2017) — 38 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