Sándor Szedmak

Computer Vision and Pattern Recognition · 2004–2006

A researcher in the Science corpus: 6 papers; trusts 37 researchers and is trusted by 847 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 40 citations into it; #1,502 of 21,575 by trust here, #2,122 by citations
• Artificial Intelligence — 18 citations into it; #2,224 of 35,345 by trust here, #5,084 by citations
• Aerospace Engineering — 7 citations into it
• Natural Language Processing — 5 citations into it
• Representation & Self-Supervised Learning — 5 citations into it; #305 of 3,145 by trust here, #688 by citations
• Signal Processing — 5 citations into it; #252 of 7,572 by trust here, #492 by citations

Whom Sándor Szedmak trusts overall

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

1. Michael I. Jordan
2. Francis R. Bach
3. David G. Lowe
4. Shotaro Akaho
5. Krystian Mikolajczyk
6. Thorsten Joachims
7. John Shawe-Taylor
8. Nello Cristianini
9. Dimitri P. Bertsekas
10. B. Taskar

Who trusts Sándor Szedmak overall

1. Yuxin Peng
2. Tae-Kyun Kim
3. Micah Hodosh
4. Saeid Motiian
5. Liwei Wang
6. Yu Zheng
7. David Roi Hardoon
8. Maithra Raghu
9. Xing Xu
10. Yanwei Fu

Papers from Sándor Szedmak's perspective

Ordered by how strongly the researchers Sándor Szedmak 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Distinctive Image Features from Scale-Invariant Keypoints
8. Histograms of oriented gradients for human detection
9. Attention is All you Need (Neural Information Processing Systems)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Canonical Correlation Analysis: An Overview with Application to Learning Methods (2004) — 3,365 citations
• The 2005 PASCAL Visual Object Classes Challenge (2005) — 525 citations
• Pascal Visual Object Classes Challenge Results (2005)
• A Correlation Approach for Automatic Image Annotation (2006) — 53 citations
• Kernel-Based Learning of Hierarchical Multilabel Classification Models (2006) — 306 citations
• Two view learning: SVM-2K, Theory and Practice (2005) — 404 citations