Elyor Kodirov
Computer Vision and Pattern Recognition · 2015–2017
A researcher in the Science corpus: 2 papers; trusts 450 researchers and is trusted by 55 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 61 citations into it; #3,700 of 21,575 by trust here, #5,205 by citations
- Artificial Intelligence — 60 citations into it; #4,067 of 35,345 by trust here, #7,241 by citations
- Representation & Self-Supervised Learning — 36 citations into it; #1,188 of 3,145 by trust here, #1,543 by citations
- Radiology, Nuclear Medicine and Imaging — 12 citations into it; #301 of 3,265 by trust here, #1,103 by citations
- Natural Language Processing — 6 citations into it
- Optimization & Training — 5 citations into it
Whom Elyor Kodirov trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Yoshua Bengio
- Geoffrey E. Hinton
- Alex Krizhevsky
- Yann LeCun
- Tomas Mikolov
- Ilya Sutskever
- Marc'Aurelio Ranzato
- Kaiming He
- Diederik P. Kingma
- Ross B. Girshick
Who trusts Elyor Kodirov overall
- Mohammad Rostami
- Shafin Rahman
- Soravit Changpinyo
- Ziming Zhang
- Yongqin Xian
- Guosen Xie
- Wei-Lun Chao
- Tristan Sylvain
- Jun Shu
- Vinay Kumar Verma
Papers from Elyor Kodirov's perspective
Ordered by how strongly the researchers Elyor Kodirov trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Going deeper with convolutions
Their papers
- Semantic Autoencoder for Zero-Shot Learning (2017) — 901 citations
- Unsupervised Domain Adaptation for Zero-Shot Learning (2015) — 400 citations