Diego Ortego

Computer Vision and Pattern Recognition · 2019–2020

A researcher in the Science corpus: 3 papers; trusts 218 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 66 citations into it; #19,918 of 21,575 by trust here, #7,421 by citations
• Artificial Intelligence — 62 citations into it; #32,294 of 35,345 by trust here, #11,327 by citations
• Representation & Self-Supervised Learning — 36 citations into it
• Optimization & Training — 18 citations into it
• Model Architectures — 5 citations into it

Whom Diego Ortego trusts overall

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

1. Kaiming He
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Diederik P. Kingma
6. Ross B. Girshick
7. Ilya Sutskever
8. Yann LeCun
9. X. Zhang
10. Jia Deng

Who trusts Diego Ortego overall

1. Eduardo Fonseca
2. Pengfei Chen
3. Junnan Li
4. Dongkai Wang
5. Kawin Ethayarajh
6. Hwanjun Song
7. Swabha Swayamdipta
8. Andrew Tao
9. Geoff Pleiss
10. Soham Deshmukh

Papers from Diego Ortego's perspective

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

Their papers

• Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning (2019) — 1,104 citations
• Unsupervised label noise modeling and loss correction (2019) — 750 citations
• Unsupervised Contrastive Learning of Sound Event Representations (2020) — 71 citations