Aggelos K. Katsaggelos

Computer Vision and Pattern Recognition · 2000–2021

A researcher in the Science corpus: 12 papers; trusts 543 researchers and is trusted by 37 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 139 citations into it; #5,766 of 21,575 by trust here, #5,116 by citations
• Computational Mechanics — 58 citations into it; #4,710 of 10,001 by trust here, #5,350 by citations
• Media Technology — 46 citations into it; #1,242 of 3,621 by trust here, #1,046 by citations
• Artificial Intelligence — 28 citations into it; #28,928 of 35,345 by trust here, #24,417 by citations
• Signal Processing — 22 citations into it; #3,577 of 7,572 by trust here, #4,201 by citations
• Cardiology and Cardiovascular Medicine — 17 citations into it
• Biomedical Engineering — 16 citations into it; #4,354 of 10,734 by trust here, #9,539 by citations
• Model Architectures — 16 citations into it

Whom Aggelos K. Katsaggelos trusts overall

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

1. Radford M. Neal
2. Geoffrey E. Hinton
3. Yann LeCun
4. Alex Krizhevsky
5. Kaiming He
6. David L. Donoho
7. Yoshua Bengio
8. Diederik P. Kingma
9. Karen Simonyan
10. Ross B. Girshick

Who trusts Aggelos K. Katsaggelos overall

1. Thierry Bouwmans
2. Ramesh Neelamani
3. Yilun Wang
4. Xian Guo
5. Xuelong Li
6. Zai Yang
7. Cameron Musco
8. Ning Xu
9. Nantheera Anantrasirichai

Papers from Aggelos K. Katsaggelos's perspective

Ordered by how strongly the researchers Aggelos K. Katsaggelos 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. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
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

• Efficient Video Object Segmentation via Network Modulation (2018) — 359 citations
• Video Super-Resolution With Convolutional Neural Networks (2016) — 626 citations
• Snapshot Compressive Imaging: Theory, Algorithms, and Applications (2021) — 332 citations
• Bayesian Compressive Sensing Using Laplace Priors (2010) — 798 citations
• Automated Atrial Fibrillation Detection using a Hybrid CNN-LSTM Network on Imbalanced ECG Datasets (2021) — 295 citations
• Using Deep Neural Networks for Inverse Problems in Imaging: Beyond Analytical Methods (2018) — 520 citations
• Gravity Spy: integrating advanced LIGO detector characterization, machine learning, and citizen science (2016) — 313 citations
• Methods for large scale machine learning (2016) — 16 citations
• Digital image restoration (2012) — 1,028 citations
• A survey of classical methods and new trends in pansharpening of multispectral images (2011) — 295 citations
• Sparse Bayesian Methods for Low-Rank Matrix Estimation (2011) — 304 citations
• Error resilient video coding techniques (2000) — 568 citations