Danilo Vasconcellos Vargas

Artificial Intelligence · 2017–2019

A researcher in the Science corpus: 6 papers; trusts 529 researchers and is trusted by 26 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 173 citations into it; #7,789 of 35,345 by trust here, #6,720 by citations
• Safety, Robustness & Interpretability — 128 citations into it; #607 of 2,251 by trust here, #604 by citations
• Computer Vision and Pattern Recognition — 81 citations into it; #6,243 of 21,575 by trust here, #5,116 by citations
• Signal Processing — 31 citations into it
• Model Architectures — 29 citations into it
• Hardware and Architecture — 17 citations into it; #1,140 of 1,551 by trust here, #1,455 by citations
• Molecular Biology — 16 citations into it
• Computer Networks and Communications — 13 citations into it

Whom Danilo Vasconcellos Vargas trusts overall

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

1. Alex Krizhevsky
2. Kaiming He
3. Geoffrey E. Hinton
4. Ian J. Goodfellow
5. Christian Szegedy
6. Karen Simonyan
7. Yoshua Bengio
8. Kenneth O. Stanley
9. Yann LeCun
10. Nicholas Carlini

Who trusts Danilo Vasconcellos Vargas overall

1. Shashank Kotyan
2. Elvis Dohmatob
3. Francesco Croce
4. Yuhang Zhou
5. Fatima Hussain
6. Jiawei Su
7. Matthias Hein
8. A. Serban
9. Anurag Arnab
10. Vishaal Munusamy Kabilan

Papers from Danilo Vasconcellos Vargas's perspective

Ordered by how strongly the researchers Danilo Vasconcellos Vargas 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. ImageNet: A large-scale hierarchical image database
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. Going deeper with convolutions

Their papers

• One Pixel Attack for Fooling Deep Neural Networks (2017) — 2,581 citations
• Model Agnostic Dual Quality Assessment for Adversarial Machine Learning and an Analysis of Current Neural Networks and Defenses (2019) — 8 citations
• Understanding the One Pixel Attack: Propagation Maps and Locality Analysis (2019) — 40 citations
• Adversarial Robustness Assessment: Why both $L_0$ and $L_\infty$ Attacks Are Necessary (2019) — 20 citations
• Evolving Robust Neural Architectures to Defend from Adversarial Attacks (2019) — 37 citations
• Spectrum-Diverse Neuroevolution With Unified Neural Models (2017) — 44 citations