Anna Monreale

Artificial Intelligence · 2009–2019

A researcher in the Science corpus: 8 papers; trusts 214 researchers and is trusted by 35 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 191 citations into it; #6,376 of 35,345 by trust here, #3,273 by citations
• Safety, Robustness & Interpretability — 123 citations into it; #555 of 2,251 by trust here, #269 by citations
• Information Systems — 49 citations into it
• Computer Vision and Pattern Recognition — 48 citations into it
• AI Ethics, Fairness & Society — 33 citations into it
• Safety Research — 29 citations into it; #3,211 of 5,829 by trust here, #2,985 by citations
• Natural Language Processing — 24 citations into it
• Computational Theory and Mathematics — 16 citations into it

Whom Anna Monreale trusts overall

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

1. Marco Tulio Ribeiro
2. Salvatore Ruggieri
3. Geoffrey E. Hinton
4. Karen Simonyan
5. Alex Krizhevsky
6. Kaiming He
7. Yoshua Bengio
8. Sameer Singh
9. Dino Pedreschi
10. Yann LeCun

Who trusts Anna Monreale overall

1. Dino Pedreschi
2. Riccardo Guidotti
3. Fosca Giannotti
4. Shubham Sharma
5. Francesco Bodria
6. Thibault Laugel
7. Q. Liao
8. Wesley Mathew
9. Xisen Jin
10. Meike Nauta

Papers from Anna Monreale's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• A Survey of Methods for Explaining Black Box Models (2018) — 5,098 citations
• Local Rule-Based Explanations of Black Box Decision Systems (2018) — 498 citations
• Discrimination- and privacy-aware patterns (2015) — 97 citations
• Meaningful Explanations of Black Box AI Decision Systems (2019) — 225 citations
• Factual and Counterfactual Explanations for Black Box Decision Making (2019) — 308 citations
• Black Box Explanation by Learning Image Exemplars in the Latent Feature Space (2019) — 77 citations
• Open the Black Box Data-Driven Explanation of Black Box Decision Systems (2018) — 39 citations
• WhereNext: a location predictor on trajectory pattern mining (2009) — 640 citations