Francesco Croce

Artificial Intelligence · 2018–2024

A researcher in the Science corpus: 8 papers; trusts 277 researchers and is trusted by 81 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 279 citations into it; #2,600 of 35,345 by trust here, #7,241 by citations
• Safety, Robustness & Interpretability — 203 citations into it; #200 of 2,251 by trust here, #992 by citations
• Computer Vision and Pattern Recognition — 84 citations into it; #8,943 of 21,575 by trust here, #11,083 by citations
• Signal Processing — 49 citations into it
• Molecular Biology — 31 citations into it; #511 of 17,773 by trust here, #8,791 by citations
• Hardware and Architecture — 27 citations into it; #1,025 of 1,551 by trust here, #1,512 by citations
• Electrical and Electronic Engineering — 20 citations into it; #384 of 16,539 by trust here, #6,657 by citations
• Evaluation & Benchmarks — 19 citations into it

Whom Francesco Croce trusts overall

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

1. Ian J. Goodfellow
2. Alex Krizhevsky
3. Nicholas Carlini
4. Geoffrey E. Hinton
5. Christian Szegedy
6. Kaiming He
7. Matthias Hein
8. Yoshua Bengio
9. Aleksander Madry
10. Diederik P. Kingma

Who trusts Francesco Croce overall

1. Sylvestre-Alvise Rebuffi
2. Tianyu Pang
3. Matthias Hein
4. Sven Gowal
5. Maksym Andriushchenko
6. Eric Wong
7. David Stutz
8. Boxi Wu
9. Xiao Yang
10. Dongxian Wu

Papers from Francesco Croce's perspective

Ordered by how strongly the researchers Francesco Croce 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. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
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. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Provable Robustness of ReLU networks via Maximization of Linear Regions (2018) — 173 citations
• Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks (2020) — 2,457 citations
• Square Attack: a query-efficient black-box adversarial attack via random search (2019) — 1,300 citations
• RobustBench: a standardized adversarial robustness benchmark (2020) — 928 citations
• Minimally distorted Adversarial Examples with a Fast Adaptive Boundary Attack (2019) — 615 citations
• Jailbreaking Leading Safety-Aligned LLMs with Simple Adaptive Attacks (2024) — 544 citations
• JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models (2024) — 556 citations
• A randomized gradient-free attack on ReLU networks (2018) — 23 citations