Georgios Georgiadis

Computer Vision and Pattern Recognition · 2018

A researcher in the Science corpus: 1 paper; trusts 733 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 40 citations into it; #7,217 of 21,575 by trust here, #19,310 by citations
• Efficiency & Compression — 33 citations into it
• Artificial Intelligence — 31 citations into it; #10,559 of 35,345 by trust here, #30,347 by citations
• Model Architectures — 8 citations into it
• Optimization & Training — 7 citations into it
• Electrical and Electronic Engineering — 6 citations into it

Whom Georgios Georgiadis trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Kaiming He
5. Yann LeCun
6. Christian Szegedy
7. Karen Simonyan
8. Ilya Sutskever
9. Sergey Ioffe
10. Diederik P. Kingma

Who trusts Georgios Georgiadis overall

1. Lorenzo Pellegrini
2. Hoagy Cunningham
3. Mark Kurtz
4. Gabriele Graffieti
5. Aidan Ewart
6. Leonard Bereska
7. Mustafa Burak Gurbuz
8. Vincenzo Lomonaco
9. Logan Riggs Smith
10. Davide Maltoni

Papers from Georgios Georgiadis's perspective

Ordered by how strongly the researchers Georgios Georgiadis 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. ImageNet: A large-scale hierarchical image database
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. Going deeper with convolutions

Their papers

• Accelerating Convolutional Neural Networks via Activation Map Compression (2018) — 86 citations