Noel Edward O'Connor

Computer Vision and Pattern Recognition · 2010–2020

A researcher in the Science corpus: 7 papers; trusts 647 researchers and is trusted by 44 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 144 citations into it; #4,202 of 21,575 by trust here, #4,469 by citations
• Artificial Intelligence — 82 citations into it; #32,295 of 35,345 by trust here, #11,327 by citations
• Representation & Self-Supervised Learning — 39 citations into it
• Evaluation & Benchmarks — 22 citations into it; #681 of 3,147 by trust here, #2,222 by citations
• Optimization & Training — 21 citations into it
• Signal Processing — 18 citations into it
• Model Architectures — 15 citations into it
• Media Technology — 5 citations into it

Whom Noel Edward O'Connor trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Kaiming He
4. Karen Simonyan
5. Yoshua Bengio
6. Ilya Sutskever
7. Diederik P. Kingma
8. Andrew Zisserman
9. Ross B. Girshick
10. Yann LeCun

Who trusts Noel Edward O'Connor overall

1. Konstantin Sofiiuk
2. Wenguan Wang
3. Won-Dong Jang
4. Soumajit Majumder
5. Suyog Dutt Jain
6. Nabil Ibtehaz
7. Ilya A. Petrov
8. Xi Chen
9. Theodora Kontogianni
10. Henghui Ding

Papers from Noel Edward O'Connor's perspective

Ordered by how strongly the researchers Noel Edward O'Connor 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. ImageNet: A large-scale hierarchical image database
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
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

• A comparative evaluation of interactive segmentation algorithms (2010) — 344 citations
• Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning (2019) — 1,104 citations
• Unsupervised label noise modeling and loss correction (2019) — 750 citations
• SalGAN: Visual Saliency Prediction with Generative Adversarial Networks (2017) — 432 citations
• Shallow and Deep Convolutional Networks for Saliency Prediction (2016) — 497 citations
• Unsupervised Contrastive Learning of Sound Event Representations (2020) — 71 citations
• Quantifying radiographic knee osteoarthritis severity using deep convolutional neural networks (2016) — 296 citations