Alex Fedorov

Computer Vision and Pattern Recognition · 2018

A researcher in the Science corpus: 1 paper; trusts 141 researchers and is trusted by 517 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 42 citations into it; #1,875 of 21,575 by trust here, #2,637 by citations
• Artificial Intelligence — 37 citations into it; #6,867 of 35,345 by trust here, #3,487 by citations
• Representation & Self-Supervised Learning — 33 citations into it; #1,168 of 3,145 by trust here, #478 by citations
• Generative Models — 21 citations into it
• Signal Processing — 7 citations into it
• Neuroscience — 5 citations into it

Whom Alex Fedorov trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Diederik P. Kingma
5. Kaiming He
6. Ilya Sutskever
7. Yann LeCun
8. Ross B. Girshick
9. Sergey Ioffe
10. Christian Szegedy

Who trusts Alex Fedorov overall

1. Xinlei Chen
2. Jiaming Song
3. Yoshua Bengio
4. Kaiming He
5. Yonglong Tian
6. Ishan Misra
7. Chen Sun
8. Stefano Ermon
9. Sergey Levine
10. Phillip Isola

Papers from Alex Fedorov's perspective

Ordered by how strongly the researchers Alex Fedorov 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. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Learning deep representations by mutual information estimation and maximization (2018) — 3,036 citations