Valentin Khrulkov

Computer Vision and Pattern Recognition · 2019–2021

A researcher in the Science corpus: 3 papers; trusts 296 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 92 citations into it; #12,955 of 21,575 by trust here, #9,872 by citations
• Artificial Intelligence — 84 citations into it; #20,809 of 35,345 by trust here, #14,438 by citations
• Model Architectures — 25 citations into it
• Representation & Self-Supervised Learning — 24 citations into it
• Generative Models — 19 citations into it
• Optimization & Training — 19 citations into it
• Biomedical Engineering — 7 citations into it

Whom Valentin Khrulkov trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Yann LeCun
4. Alex Krizhevsky
5. Diederik P. Kingma
6. Yoshua Bengio
7. Karen Simonyan
8. Ilya Sutskever
9. Jia Deng
10. Andrew Zisserman

Who trusts Valentin Khrulkov overall

1. Davis Wertheimer
2. Ines Chami
3. Léo Grinsztajn
4. Luming Tang
5. Rex Ying
6. C. Yuhua

Papers from Valentin Khrulkov's perspective

Ordered by how strongly the researchers Valentin Khrulkov trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
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

• Label-Efficient Semantic Segmentation with Diffusion Models (2021) — 723 citations
• Revisiting Deep Learning Models for Tabular Data (2021) — 1,551 citations
• Hyperbolic Image Embeddings (2019) — 411 citations