Dmytro Mishkin

Computer Vision and Pattern Recognition · 2015–2020

A researcher in the Science corpus: 8 papers; trusts 916 researchers and is trusted by 333 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 246 citations into it; #2,223 of 21,575 by trust here, #3,538 by citations
• Artificial Intelligence — 101 citations into it; #3,931 of 35,345 by trust here, #8,439 by citations
• Aerospace Engineering — 80 citations into it; #1,493 of 6,421 by trust here, #1,573 by citations
• Model Architectures — 57 citations into it
• Optimization & Training — 41 citations into it; #467 of 2,601 by trust here, #799 by citations
• Media Technology — 31 citations into it; #2,590 of 3,621 by trust here, #1,498 by citations
• Evaluation & Benchmarks — 17 citations into it; #2,395 of 3,147 by trust here, #2,244 by citations
• Reinforcement Learning — 15 citations into it

Whom Dmytro Mishkin trusts overall

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

1. Alex Krizhevsky
2. Kaiming He
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Yann LeCun
6. Diederik P. Kingma
7. Karen Simonyan
8. Christian Szegedy
9. Ilya Sutskever
10. David G. Lowe

Who trusts Dmytro Mishkin overall

1. Dan Hendrycks
2. Kamaledin Ghiasi-Shirazi
3. Kevin Gimpel
4. Yann Dauphin
5. Sam Schoenholz
6. Jeffrey Pennington
7. Waseem Rawat
8. Samuli Laine
9. Juri Opitz
10. Jingdong Wang

Papers from Dmytro Mishkin's perspective

Ordered by how strongly the researchers Dmytro Mishkin 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. ImageNet: A large-scale hierarchical image database
5. Gradient-based learning applied to document recognition
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

• All you need is a good init (2015) — 655 citations
• Working hard to know your neighbor's margins: Local descriptor learning loss (2017) — 799 citations
• Benchmarking Classic and Learned Navigation in Complex 3D Environments (2019) — 78 citations
• Image Matching Across Wide Baselines: From Paper to Practice (2020) — 469 citations
• Kornia: an Open Source Differentiable Computer Vision Library for PyTorch (2019) — 459 citations
• Systematic evaluation of CNN advances on the ImageNet (2016) — 123 citations
• Systematic evaluation of convolution neural network advances on the Imagenet (2017) — 265 citations
• DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks (2017) — 1,703 citations