G. Tziritas

Computer Vision and Pattern Recognition · 2007–2018

A researcher in the Science corpus: 5 papers; trusts 118 researchers and is trusted by 237 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 65 citations into it; #2,572 of 21,575 by trust here, #7,027 by citations
• Computer Graphics and Computer-Aided Design — 15 citations into it; #271 of 1,584 by trust here, #976 by citations
• Artificial Intelligence — 11 citations into it; #8,494 of 35,345 by trust here, #19,439 by citations
• Computational Mechanics — 11 citations into it; #885 of 10,001 by trust here, #1,391 by citations
• Computer Networks and Communications — 11 citations into it
• Optimization & Training — 7 citations into it

Whom G. Tziritas trusts overall

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

1. Vladimir Kolmogorov
2. Dimitri P. Bertsekas
3. William T. Freeman
4. Marcelo Bertalmío
5. Michael Ashikhmin
6. Alexei A. Efros
7. Pedro F. Felzenszwalb
8. Vivek Kwatra
9. Nikos Komodakis
10. Javier Portilla

Who trusts G. Tziritas overall

1. Kaiming He
2. Jia-Bin Huang
3. Nikos Komodakis
4. Aristeidis Sotiras
5. Nikos Paragios
6. Sebastian Nowozin
7. Ben Glocker
8. Denis Fortun
9. Johannes Peter Kopf
10. Alexander M. Rush

Papers from G. Tziritas's perspective

Ordered by how strongly the researchers G. Tziritas 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. ImageNet: A large-scale hierarchical image database
4. Gradient-based learning applied to document recognition
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Image Completion Using Efficient Belief Propagation Via Priority Scheduling and Dynamic Pruning (2007) — 377 citations
• MRF Energy Minimization and Beyond via Dual Decomposition (2011) — 338 citations
• MRF Optimization via Dual Decomposition: Message-Passing Revisited (2007) — 339 citations
• Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved? (2018) — 2,285 citations
• Dense image registration through MRFs and efficient linear programming (2008) — 482 citations