Konstantin Shmelkov

Computer Vision and Pattern Recognition · 2017–2018

A researcher in the Science corpus: 3 papers; trusts 649 researchers and is trusted by 288 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 90 citations into it; #2,319 of 21,575 by trust here, #3,525 by citations
• Artificial Intelligence — 55 citations into it; #3,649 of 35,345 by trust here, #5,720 by citations
• Generative Models — 31 citations into it
• Representation & Self-Supervised Learning — 15 citations into it
• Optimization & Training — 13 citations into it
• Model Architectures — 10 citations into it
• Media Technology — 6 citations into it; #1,541 of 3,621 by trust here, #2,178 by citations
• Statistical and Nonlinear Physics — 5 citations into it

Whom Konstantin Shmelkov trusts overall

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

1. Ross B. Girshick
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Kaiming He
5. Yann LeCun
6. Yoshua Bengio
7. Diederik P. Kingma
8. Ilya Sutskever
9. Shaoqing Ren
10. Karen Simonyan

Who trusts Konstantin Shmelkov overall

1. Rahaf Aljundi
2. Umberto Michieli
3. GIANG NGUYEN
4. Ali Borji
5. K. J. Joseph
6. Pietro Zanuttigh
7. Tyler L. Hayes
8. Deboleena Roy
9. Dawei Li
10. Arun Mallya

Papers from Konstantin Shmelkov's perspective

Ordered by how strongly the researchers Konstantin Shmelkov 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

• Incremental Learning of Object Detectors without Catastrophic Forgetting (2017) — 607 citations
• BlitzNet: A Real-Time Deep Network for Scene Understanding (2017) — 211 citations
• How good is my GAN? (2018) — 387 citations