George Barbastathis

Computer Vision and Pattern Recognition · 2005–2020

A researcher in the Science corpus: 6 papers; trusts 85 researchers and is trusted by 25 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 49 citations into it
• Atomic and Molecular Physics, and Optics — 17 citations into it; #739 of 7,135 by trust here, #2,804 by citations
• Biomedical Engineering — 16 citations into it
• Media Technology — 16 citations into it
• Computational Mechanics — 15 citations into it
• Model Architectures — 10 citations into it
• Electrical and Electronic Engineering — 7 citations into it
• Radiation — 7 citations into it; #24 of 407 by trust here, #223 by citations

Whom George Barbastathis trusts overall

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

1. Veit Elser
2. E.J. Candes
3. Yair Rivenson
4. Yann LeCun
5. Michael Elad
6. Alex Krizhevsky
7. Karol Gregor
8. Geoffrey E. Hinton
9. Terence Tao
10. Yibo Zhang

Who trusts George Barbastathis overall

1. Gordon Wetzstein
2. Yifan Peng
3. Chao Zuo
4. Xin Yuan
5. Suyeon Choi
6. Fei Wang
7. Aydogan Ozcan
8. Jiaming Qian
9. Meng Lyu
10. Nitish Padmanaban

Papers from George Barbastathis's perspective

Ordered by how strongly the researchers George Barbastathis 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. Gradient-based learning applied to document recognition
5. ImageNet Large Scale Visual Recognition Challenge
6. Learning Multiple Layers of Features from Tiny Images
7. Attention is All you Need (Neural Information Processing Systems)
8. Long Short-Term Memory
9. ImageNet: A large-scale hierarchical image database
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Origami fabrication of nanostructured, three-dimensional devices: Electrochemical capacitors with carbon electrodes (2006) — 133 citations
• Imaging through glass diffusers using densely connected convolutional networks (2017) — 345 citations
• Dynamic pull-in of parallel-plate and torsional electrostatic MEMS actuators (2005) — 161 citations
• On the use of deep learning for computational imaging (2020) — 574 citations
• Phase imaging with an untrained neural network (2020) — 369 citations
• Lensless computational imaging through deep learning (2017) — 588 citations