J. Alison Noble

Computer Vision and Pattern Recognition · The Lancet · 2013–2018

A researcher in the Science corpus: 7 papers; trusts 410 researchers and is trusted by 18 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 83 citations into it; #7,070 of 21,575 by trust here, #8,205 by citations
• Artificial Intelligence — 30 citations into it; #23,018 of 35,345 by trust here, #28,481 by citations
• Model Architectures — 8 citations into it
• Media Technology — 6 citations into it; #2,051 of 3,621 by trust here, #2,178 by citations

Whom J. Alison Noble trusts overall

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

1. Andrew Zisserman
2. Kaiming He
3. Alex Krizhevsky
4. Victor S. Lempitsky
5. Karen Simonyan
6. Andrea Vedaldi
7. Ross B. Girshick
8. Geoffrey E. Hinton
9. Yann LeCun
10. Shaoqing Ren

Who trusts J. Alison Noble overall

1. Carlos Arteta
2. Massimo Minervini
3. Chetan L. Srinidhi
4. Muhammad Imran Razzak
5. Martin Weigert
6. Giuseppe Amato
7. Daniel Oñoro-Rubio
8. Mario Valerio Giuffrida
9. Hanno Scharr
10. Seyed Hamid Rezatofighi

Papers from J. Alison Noble's perspective

Ordered by how strongly the researchers J. Alison Noble 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. ImageNet: A large-scale hierarchical image database
6. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
7. Long Short-Term Memory
8. ImageNet Large Scale Visual Recognition Challenge
9. Attention is All you Need (Neural Information Processing Systems)
10. Going deeper with convolutions

Their papers

• Interactive Object Counting (2014) — 189 citations
• Learning to Detect Partially Overlapping Instances (2013) — 47 citations
• Protocol and quality assurance for carotid imaging in 100,000 participants of UK Biobank: development and assessment (2017) — 40 citations
• Weakly-supervised convolutional neural networks for multimodal image registration (2018) — 442 citations
• Microscopy cell counting and detection with fully convolutional regression networks (2018) — 500 citations
• Intraoperative Organ Motion Models with an Ensemble of Conditional Generative Adversarial Networks (2017) — 16 citations
• International standards for fetal growth based on serial ultrasound measurements: the Fetal Growth Longitudinal Study of the INTERGROWTH-21st Project. (2014) — 775 citations