Iryna Korshunova

Computer Vision and Pattern Recognition · 2016–2018

A researcher in the Science corpus: 3 papers; trusts 265 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 46 citations into it; #7,345 of 21,575 by trust here, #7,249 by citations
• Artificial Intelligence — 17 citations into it; #20,351 of 35,345 by trust here, #25,809 by citations
• Model Architectures — 12 citations into it
• Signal Processing — 11 citations into it; #4,181 of 7,572 by trust here, #5,086 by citations
• Generative Models — 10 citations into it
• Cognitive Neuroscience — 9 citations into it
• Efficiency & Compression — 6 citations into it
• Computer Graphics and Computer-Aided Design — 5 citations into it

Whom Iryna Korshunova trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yann LeCun
4. Alex Krizhevsky
5. Karen Simonyan
6. Yoshua Bengio
7. Andrew Zisserman
8. Kaiming He
9. Jimmy Ba
10. Shaoqing Ren

Who trusts Iryna Korshunova overall

1. Yisroel Mirsky
2. Sidak Pal Singh
3. Pavel Korshunov
4. Hasam Khalid
5. Yuval Nirkin
6. Yuhao Zhu
7. Yongcheng Jing
8. Momina Masood
9. Shuyang Gu
10. Dan Alistarh

Papers from Iryna Korshunova's perspective

Ordered by how strongly the researchers Iryna Korshunova 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. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
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

• Music transcription modelling and composition using deep learning (2016) — 190 citations
• Fast Face-Swap Using Convolutional Neural Networks (2016) — 454 citations
• Faster gaze prediction with dense networks and Fisher pruning (2018) — 231 citations