Orestis Vantzos

Computer Vision and Pattern Recognition · 2015

A researcher in the Science corpus: 2 papers; trusts 611 researchers and is trusted by 34 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 65 citations into it; #4,447 of 21,575 by trust here, #8,502 by citations
• Artificial Intelligence — 29 citations into it; #12,085 of 35,345 by trust here, #22,463 by citations
• Model Architectures — 11 citations into it; #909 of 2,278 by trust here, #1,826 by citations
• Media Technology — 8 citations into it
• Computational Theory and Mathematics — 6 citations into it
• Representation & Self-Supervised Learning — 6 citations into it
• Natural Language Processing — 5 citations into it

Whom Orestis Vantzos trusts overall

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

1. Yann LeCun
2. Kaiming He
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. David G. Lowe
6. Ross B. Girshick
7. Yoshua Bengio
8. L. Eon Bottou
9. Karen Simonyan
10. Jitendra Malik

Who trusts Orestis Vantzos overall

1. P. Li
2. Zhiwu Huang
3. Lei Huang
4. Breton L. Minnehan
5. Jiangtao Xie
6. Qilong Wang
7. Luc Van Gool
8. Tsung-Yu Lin
9. Zilin Gao
10. Tan Yu

Papers from Orestis Vantzos's perspective

Ordered by how strongly the researchers Orestis Vantzos 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. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Matrix Backpropagation for Deep Networks with Structured Layers (2015) — 317 citations
• Training Deep Networks with Structured Layers by Matrix Backpropagation (2015) — 92 citations