Mateo Rojas-Carulla

Artificial Intelligence · 2015–2017

A researcher in the Science corpus: 5 papers; trusts 122 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 86 citations into it; #15,572 of 35,345 by trust here, #5,120 by citations
• Representation & Self-Supervised Learning — 44 citations into it; #1,606 of 3,145 by trust here, #1,358 by citations
• Computer Vision and Pattern Recognition — 26 citations into it; #13,728 of 21,575 by trust here, #8,952 by citations
• Causal Inference & Discovery — 24 citations into it; #169 of 364 by trust here, #80 by citations
• Probabilistic & Bayesian Methods — 24 citations into it
• Statistics and Probability — 17 citations into it
• Optimization & Training — 13 citations into it
• Computational Theory and Mathematics — 8 citations into it

Whom Mateo Rojas-Carulla trusts overall

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

1. Bernhard Schölkopf
2. Arthur Gretton
3. Robert Tibshirani
4. Alex Krizhevsky
5. Geoffrey E. Hinton
6. Jonathan Baxter
7. Diederik P. Kingma
8. Subir Ghosh
9. Radford M. Neal
10. Kenji Fukumizu

Who trusts Mateo Rojas-Carulla overall

1. Bernhard Schölkopf
2. Yoshua Bengio
3. Francesco Locatello
4. Julius von Kügelgen
5. Anirudh Goyal
6. Silvia Chiappa
7. Adarsh Subbaswamy
8. Divyat Mahajan
9. Stefan Bauer
10. Harvineet Singh

Papers from Mateo Rojas-Carulla's perspective

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

Their papers

• Discriminative k-shot learning using probabilistic models (2017) — 71 citations
• Learning Independent Causal Mechanisms (2017) — 205 citations
• Avoiding Discrimination through Causal Reasoning (2017) — 640 citations
• Causal Transfer in Machine Learning (2015) — 23 citations
• Invariant Models for Causal Transfer Learning (2015) — 443 citations