Giancarlo Pellegrino

Artificial Intelligence · 2018

A researcher in the Science corpus: 3 papers; trusts 330 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 98 citations into it; #34,091 of 35,345 by trust here, #20,836 by citations
• Safety, Robustness & Interpretability — 93 citations into it; #1,662 of 2,251 by trust here, #1,512 by citations
• Computer Vision and Pattern Recognition — 58 citations into it; #15,755 of 21,575 by trust here, #11,950 by citations
• Signal Processing — 38 citations into it
• Hardware and Architecture — 14 citations into it
• Model Architectures — 10 citations into it
• Electrical and Electronic Engineering — 5 citations into it

Whom Giancarlo Pellegrino trusts overall

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

1. Ross B. Girshick
2. Karen Simonyan
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Kaiming He
6. Ian J. Goodfellow
7. Yoshua Bengio
8. Christian Szegedy
9. Andrew Zisserman
10. Yann LeCun

Who trusts Giancarlo Pellegrino overall

1. Eugene Bagdasarian

Papers from Giancarlo Pellegrino's perspective

Ordered by how strongly the researchers Giancarlo Pellegrino 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. ImageNet: A large-scale hierarchical image database
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Attention is All you Need (Neural Information Processing Systems)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• SentiNet: Detecting Localized Universal Attacks Against Deep Learning Systems (2018) — 352 citations
• AdVersarial: Perceptual Ad Blocking meets Adversarial Machine Learning (2018) — 65 citations
• SentiNet: Detecting Physical Attacks Against Deep Learning Systems (2018) — 111 citations