Stefano Teso

Artificial Intelligence · 2019–2020

A researcher in the Science corpus: 2 papers; trusts 29 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 54 citations into it; #14,113 of 35,345 by trust here, #18,586 by citations
• Safety, Robustness & Interpretability — 23 citations into it; #952 of 2,251 by trust here, #1,589 by citations
• Computer Vision and Pattern Recognition — 18 citations into it
• Management Science and Operations Research — 8 citations into it
• Statistical Learning Theory — 7 citations into it
• Social Psychology — 7 citations into it
• Natural Language Processing — 6 citations into it; #2,724 of 5,688 by trust here, #4,682 by citations
• Representation & Self-Supervised Learning — 6 citations into it

Whom Stefano Teso trusts overall

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

1. David Cohn
2. Nicholas A. Roy
3. Marco Tulio Ribeiro
4. Scott M. Lundberg
5. Andrew Slavin Ross
6. Omar Zaidan
7. Andrew McCallum
8. Kevin Small
9. Osbert Bastani
10. Burr Settles

Who trusts Stefano Teso overall

1. Patrick Schramowski
2. Wolfgang Stammer
3. Gesina Schwalbe
4. Piyawat Lertvittayakumjorn
5. Bettina Finzel
6. Kristian Kersting
7. Lin Guan
8. Francesca Toni
9. Andreas Holzinger

Papers from Stefano Teso's perspective

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

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

Their papers

• Making deep neural networks right for the right scientific reasons by interacting with their explanations (2020) — 250 citations
• Explanatory Interactive Machine Learning (2019) — 264 citations