Riccardo Guidotti

Artificial Intelligence · 2018–2023

A researcher in the Science corpus: 9 papers; trusts 300 researchers and is trusted by 32 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 423 citations into it; #6,951 of 35,345 by trust here, #3,400 by citations
• Safety, Robustness & Interpretability — 251 citations into it; #563 of 2,251 by trust here, #269 by citations
• Computer Vision and Pattern Recognition — 113 citations into it
• Information Systems — 72 citations into it
• Safety Research — 55 citations into it
• Natural Language Processing — 52 citations into it
• AI Ethics, Fairness & Society — 40 citations into it
• Computational Theory and Mathematics — 26 citations into it

Whom Riccardo Guidotti trusts overall

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

1. Marco Tulio Ribeiro
2. Salvatore Ruggieri
3. Geoffrey E. Hinton
4. J. Ross Quinlan
5. Karen Simonyan
6. Robert Tibshirani
7. Kaiming He
8. Alex Krizhevsky
9. Yoshua Bengio
10. Sameer Singh

Who trusts Riccardo Guidotti overall

1. Dino Pedreschi
2. Fosca Giannotti
3. Q. Liao
4. Francesco Bodria
5. Shubham Sharma
6. Thibault Laugel
7. Xisen Jin
8. Meike Nauta
9. Amr Adel
10. Sarah Wiegreffe

Papers from Riccardo Guidotti's perspective

Ordered by how strongly the researchers Riccardo Guidotti 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. Attention is All you Need (Neural Information Processing Systems)
5. Gradient-based learning applied to document recognition
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
• Benchmarking and survey of explanation methods for black box models (2021) — 345 citations
• Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence (2023) — 1,437 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
• On The Stability of Interpretable Models (2018) — 38 citations