Ivan Evtimov

Artificial Intelligence · 2017–2023

A researcher in the Science corpus: 6 papers; trusts 45 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 146 citations into it; #23,600 of 35,345 by trust here, #3,384 by citations
• Safety, Robustness & Interpretability — 88 citations into it; #1,762 of 2,251 by trust here, #288 by citations
• Computer Vision and Pattern Recognition — 81 citations into it
• Signal Processing — 24 citations into it; #5,839 of 7,572 by trust here, #812 by citations
• Representation & Self-Supervised Learning — 6 citations into it

Whom Ivan Evtimov trusts overall

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

1. Ian J. Goodfellow
2. Alex Krizhevsky
3. Christian Szegedy
4. Geoffrey E. Hinton
5. Nicolas Papernot
6. Nicholas Carlini
7. Yann LeCun
8. Ross B. Girshick
9. Yoshua Bengio
10. Ilya Sutskever

Who trusts Ivan Evtimov overall

1. Stepan Alekseevich Komkov
2. Jiajun Lu
3. Florian Tramèr
4. Edward Chou
5. Aleksandr Petiushko
6. Kevin Eykholt
7. Bo Li
8. Shang-Tse Chen
9. Tom Everitt
10. Pin-Yu Chen

Papers from Ivan Evtimov's perspective

Ordered by how strongly the researchers Ivan Evtimov 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. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
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. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Robust Physical-World Attacks on Deep Learning Visual Classification (2018) — 2,198 citations
• Robust Physical-World Attacks on Deep Learning Models (2017) — 621 citations
• Code Llama: Open Foundation Models for Code (2023) — 3,394 citations
• Note on Attacking Object Detectors with Adversarial Stickers (2017) — 37 citations
• Physical Adversarial Examples for Object Detectors (2018) — 554 citations
• Robust Physical-World Attacks on Machine Learning Models (2017) — 343 citations