Yair Rivenson

Computer Vision and Pattern Recognition · 2010–2020

A researcher in the Science corpus: 9 papers; trusts 30 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 79 citations into it; #19,952 of 21,575 by trust here, #10,500 by citations
• Artificial Intelligence — 39 citations into it
• Atomic and Molecular Physics, and Optics — 31 citations into it; #5,079 of 7,135 by trust here, #2,383 by citations
• Optimization & Training — 12 citations into it
• Model Architectures — 10 citations into it
• Computational Mechanics — 8 citations into it
• Management Science and Operations Research — 6 citations into it

Whom Yair Rivenson trusts overall

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

1. Kaiming He
2. Diederik P. Kingma
3. X. Zhang
4. Joseph W. Goodman
5. E.J. Candes
6. Shaoqing Ren
7. Geoffrey E. Hinton
8. Yann LeCun
9. Alex Krizhevsky
10. Yoshua Bengio

Who trusts Yair Rivenson overall

1. George Barbastathis
2. Gordon Wetzstein
3. C. Belthangady
4. Goëry Genty
5. Wei Ma
6. Aydogan Ozcan
7. Yifan Peng
8. Shijie Feng
9. Kan Yao
10. Loic A. Royer

Papers from Yair Rivenson's perspective

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

Their papers

• All-optical machine learning using diffractive deep neural networks (2018) — 2,293 citations
• Deep learning enables cross-modality super-resolution in fluorescence microscopy (2018) — 713 citations
• Compressive Fresnel Holography (2010) — 176 citations
• Deep learning-based transformation of H&E stained tissues into special stains (2020) — 316 citations
• Virtual histological staining of unlabelled tissue-autofluorescence images via deep learning (2018) — 612 citations
• PhaseStain: the digital staining of label-free quantitative phase microscopy images using deep learning (2018) — 388 citations
• Deep Learning Microscopy: Enhancing Resolution, Field-of-View and Depth-of-Field of Optical Microscopy Images Using Neural Networks (2017) — 403 citations
• Phase recovery and holographic image reconstruction using deep learning in neural networks (2017) — 955 citations
• Roadmap on optical security (2016) — 398 citations