George Trigeorgis

Computer Vision and Pattern Recognition · 2015–2017

A researcher in the Science corpus: 7 papers; trusts 544 researchers and is trusted by 51 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 163 citations into it; #10,564 of 21,575 by trust here, #6,485 by citations
• Artificial Intelligence — 69 citations into it; #5,917 of 35,345 by trust here, #4,205 by citations
• Signal Processing — 42 citations into it; #6,937 of 7,572 by trust here, #2,525 by citations
• Model Architectures — 21 citations into it
• Optimization & Training — 19 citations into it
• Representation & Self-Supervised Learning — 16 citations into it; #929 of 3,145 by trust here, #677 by citations
• Experimental and Cognitive Psychology — 13 citations into it; #5,631 of 9,731 by trust here, #2,565 by citations
• Aerospace Engineering — 12 citations into it

Whom George Trigeorgis trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Alex Krizhevsky
4. Ilya Sutskever
5. Yoshua Bengio
6. Kaiming He
7. Yann LeCun
8. David G. Lowe
9. J. Urgen Schmidhuber
10. Jimmy Ba

Who trusts George Trigeorgis overall

1. Sebastian Ruder
2. Xuanyi Dong
3. Kuniaki Saito
4. Mirco Ravanelli
5. Yoshua Bengio
6. Amir Zadeh
7. Riza Alp Güler
8. Adrian Bulat
9. Da Li
10. Jian Liang

Papers from George Trigeorgis's perspective

Ordered by how strongly the researchers George Trigeorgis trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
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. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
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

• Domain Separation Networks (2016) — 1,611 citations
• DenseReg: Fully Convolutional Dense Shape Regression In-the-Wild (2016) — 206 citations
• Mnemonic Descent Method: A Recurrent Process Applied for End-to-End Face Alignment (2016) — 362 citations
• 3D Face Morphable Models "In-the-Wild" (2017) — 192 citations
• Adieu features? End-to-end speech emotion recognition using a deep convolutional recurrent network (2016) — 895 citations
• End-to-End Multimodal Emotion Recognition Using Deep Neural Networks (2017) — 648 citations
• A Deep Matrix Factorization Method for Learning Attribute Representations (2015) — 338 citations