Valentina Zantedeschi

Artificial Intelligence · 2017–2020

A researcher in the Science corpus: 5 papers; trusts 138 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 159 citations into it; #10,260 of 35,345 by trust here, #11,624 by citations
• Safety, Robustness & Interpretability — 108 citations into it; #691 of 2,251 by trust here, #1,312 by citations
• Computer Vision and Pattern Recognition — 49 citations into it
• Signal Processing — 31 citations into it; #2,007 of 7,572 by trust here, #2,343 by citations
• Optimization & Training — 25 citations into it
• Computer Networks and Communications — 24 citations into it
• Computational Mechanics — 21 citations into it
• Information Systems — 11 citations into it

Whom Valentina Zantedeschi trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Yoshua Bengio
4. Yann LeCun
5. J. Ross Quinlan
6. Kaiming He
7. Christian Szegedy
8. Ian J. Goodfellow
9. Ilya Sutskever
10. Diederik P. Kingma

Who trusts Valentina Zantedeschi overall

1. David Stutz
2. Nicholas Carlini
3. Xuanqing Liu
4. Maria-Irina Nicolae
5. Haifeng Qian
6. Pin-Yu Chen
7. Huan Zhang
8. Ali Shafahi
9. Matthias Hein
10. Emilio Carrizosa

Papers from Valentina Zantedeschi's perspective

Ordered by how strongly the researchers Valentina Zantedeschi 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. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Going deeper with convolutions

Their papers

• Efficient Defenses Against Adversarial Attacks (2017) — 315 citations
• Adversarial Robustness Toolbox v1.0.0 (2018) — 573 citations
• Learning Binary Trees via Sparse Relaxation (2020) — 4 citations
• Fully Decentralized Joint Learning of Personalized Models and Collaboration Graphs (2019) — 90 citations
• Adversarial Robustness Toolbox v0.2.2 (2018) — 59 citations