Ioannis D. Apostolopoulos

Computer Vision and Pattern Recognition · 2020

A researcher in the Science corpus: 2 papers; trusts 50 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 17 citations into it; #17,435 of 21,575 by trust here, #20,271 by citations
• Artificial Intelligence — 14 citations into it; #14,066 of 35,345 by trust here, #12,761 by citations
• Model Architectures — 7 citations into it
• Radiology, Nuclear Medicine and Imaging — 6 citations into it; #752 of 3,265 by trust here, #1,169 by citations

Whom Ioannis D. Apostolopoulos trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. François Chollet
4. Karen Simonyan
5. Yann LeCun
6. Alex Krizhevsky
7. Joseph Paul Cohen
8. Yoshua Bengio
9. Christian Szegedy
10. Vinod Nair

Who trusts Ioannis D. Apostolopoulos overall

1. Ali Narin
2. Gonçalo Marques
3. Asmaa Abbas
4. N. Das
5. T. Ozturk
6. M. A. Elaziz
7. Joseph Paul Cohen
8. Muhammad Enamul Hoque Chowdhury
9. S. Latif
10. Ceren Kaya

Papers from Ioannis D. Apostolopoulos's perspective

Ordered by how strongly the researchers Ioannis D. Apostolopoulos 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. 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

• Extracting Possibly Representative COVID-19 Biomarkers from X-ray Images with Deep Learning Approach and Image Data Related to Pulmonary Diseases (2020) — 312 citations
• Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks (2020) — 2,008 citations