John Guibas

Computer Vision and Pattern Recognition · 2017–2021

A researcher in the Science corpus: 2 papers; trusts 34 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 34 citations into it; #13,881 of 21,575 by trust here, #12,419 by citations
• Artificial Intelligence — 28 citations into it; #22,419 of 35,345 by trust here, #23,077 by citations
• Generative Models — 5 citations into it; #1,300 of 1,956 by trust here, #1,577 by citations

Whom John Guibas trusts overall

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

1. Kaiming He
2. Robert Tibshirani
3. Alec Radford
4. Geoffrey E. Hinton
5. Alex Krizhevsky
6. Diederik P. Kingma
7. Yoshua Bengio
8. Ross B. Girshick
9. Yann LeCun
10. Ilya Sutskever

Who trusts John Guibas overall

1. Arvid Lundervold
2. Xin Yi
3. Justin Ker
4. Nima Tajbakhsh
5. Salome Kazeminia
6. Jaideep Pathak
7. Zhihan Gao
8. Lei Chen
9. Boris Bonev
10. Ekta Walia

Papers from John Guibas's perspective

Ordered by how strongly the researchers John Guibas 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. Attention is All you Need (Neural Information Processing Systems)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Synthetic Medical Images from Dual Generative Adversarial Networks (2017) — 186 citations
• Adaptive Fourier Neural Operators: Efficient Token Mixers for Transformers (2021) — 388 citations