Federico Cerutti

Artificial Intelligence · 2017–2020

A researcher in the Science corpus: 7 papers; trusts 49 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 113 citations into it
• Safety, Robustness & Interpretability — 55 citations into it
• Computer Vision and Pattern Recognition — 33 citations into it
• Probabilistic & Bayesian Methods — 29 citations into it
• Signal Processing — 9 citations into it
• Statistics and Probability — 7 citations into it
• Computer Networks and Communications — 6 citations into it
• Information Theory — 5 citations into it

Whom Federico Cerutti trusts overall

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

1. Geoffrey E. Hinton
2. Radford M. Neal
3. Diederik P. Kingma
4. Yann LeCun
5. Alex Krizhevsky
6. Yoshua Bengio
7. Judea Pearl
8. L. Eon Bottou
9. Ilya Sutskever
10. Ian J. Goodfellow

Who trusts Federico Cerutti overall

1. Alun David Preece
2. Richard J. Tomsett

Papers from Federico Cerutti's perspective

Ordered by how strongly the researchers Federico Cerutti 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. ImageNet Large Scale Visual Recognition Challenge
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Interpretability of deep learning models: A survey of results (2017) — 469 citations
• Rapid Trust Calibration through Interpretable and Uncertainty-Aware AI (2020) — 175 citations
• Uncertainty-Aware Deep Classifiers Using Generative Models (2020) — 90 citations
• Explainable AI for Intelligence Augmentation in Multi-Domain Operations (2019) — 17 citations
• Uncertainty-aware situational understanding (2019) — 3 citations
• Uncertainty Aware AI ML: Why and How (2018) — 22 citations
• Cognitive computing for coalition situational understanding (2017) — 20 citations