Sara Magliacane

Artificial Intelligence · with Claassen · 2016–2017

A researcher in the Science corpus: 4 papers; trusts 51 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 60 citations into it; #16,044 of 35,345 by trust here, #17,110 by citations
• Causal Inference & Discovery — 49 citations into it; #193 of 364 by trust here, #249 by citations
• Representation & Self-Supervised Learning — 20 citations into it; #2,920 of 3,145 by trust here, #2,299 by citations
• Statistics and Probability — 19 citations into it
• Probabilistic & Bayesian Methods — 18 citations into it
• Molecular Biology — 17 citations into it
• Information Systems — 8 citations into it
• Computational Theory and Mathematics — 7 citations into it

Whom Sara Magliacane trusts overall

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

1. Judea Pearl
2. Tom L. Burr
3. Joris M. Mooij
4. Kun Zhang
5. Jiji Zhang
6. Bernhard Schölkopf
7. Elias Bareinboim
8. Jan Peters
9. Peter Spirtes
10. Antti Hyttinen

Who trusts Sara Magliacane overall

1. Bernhard Schölkopf
2. Joris M. Mooij
3. Yuhao Wang
4. Tom Claassen
5. Thijs van Ommen
6. Stephan Bongers
7. Fattaneh Jabbari
8. Chandler Squires
9. Divyat Mahajan
10. Christina Heinze-Deml

Papers from Sara Magliacane's perspective

Ordered by how strongly the researchers Sara Magliacane 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. Learning Multiple Layers of Features from Tiny Images
8. GENERATIVE ADVERSARIAL NETS
9. ImageNet: A large-scale hierarchical image database
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions (2017) — 278 citations
• Ancestral Causal Inference (2016) — 70 citations
• Causal Transfer Learning (2017) — 17 citations
• Joint Causal Inference from Multiple Contexts (2016) — 300 citations