Kevin McGuinness

Computer Vision and Pattern Recognition · 2010–2020

A researcher in the Science corpus: 8 papers; trusts 714 researchers and is trusted by 44 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 161 citations into it; #4,201 of 21,575 by trust here, #4,402 by citations
• Artificial Intelligence — 96 citations into it; #32,292 of 35,345 by trust here, #11,072 by citations
• Representation & Self-Supervised Learning — 45 citations into it
• Evaluation & Benchmarks — 25 citations into it; #680 of 3,147 by trust here, #2,222 by citations
• Optimization & Training — 24 citations into it
• Signal Processing — 21 citations into it
• Model Architectures — 16 citations into it
• Media Technology — 7 citations into it

Whom Kevin McGuinness trusts overall

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

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

Who trusts Kevin McGuinness overall

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

Papers from Kevin McGuinness's perspective

Ordered by how strongly the researchers Kevin McGuinness 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
• Learning Multiple Views with Orthogonal Denoising Autoencoders (2016) — 20 citations