Vadim Lebedev

Computer Vision and Pattern Recognition · 2014–2016

A researcher in the Science corpus: 3 papers; trusts 426 researchers and is trusted by 742 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 54 citations into it; #1,603 of 21,575 by trust here, #2,478 by citations
• Artificial Intelligence — 33 citations into it; #2,903 of 35,345 by trust here, #4,747 by citations
• Efficiency & Compression — 17 citations into it; #86 of 1,264 by trust here, #245 by citations
• Model Architectures — 14 citations into it
• Signal Processing — 10 citations into it
• Generative Models — 9 citations into it; #481 of 1,956 by trust here, #661 by citations
• Optimization & Training — 8 citations into it
• Computational Mathematics — 6 citations into it; #318 of 612 by trust here, #49 by citations

Whom Vadim Lebedev trusts overall

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

1. Yann LeCun
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Karen Simonyan
5. Andrew Zisserman
6. Andrea Vedaldi
7. Kaiming He
8. Yoshua Bengio
9. Ilya Sutskever
10. Jimmy Ba

Who trusts Vadim Lebedev overall

1. Wujie Wen
2. Huan Wang
3. Frederick Tung
4. Georgios Georgiadis
5. Shaohui Lin
6. Zhuang Liu
7. Adam Polyak
8. Michael Zhu
9. Mikhail Figurnov
10. Jiaxiang Wu

Papers from Vadim Lebedev's perspective

Ordered by how strongly the researchers Vadim Lebedev 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Texture Networks: Feed-forward Synthesis of Textures and Stylized Images (2016) — 1,012 citations
• Speeding-up Convolutional Neural Networks Using Fine-tuned CP-Decomposition (2014) — 952 citations
• Fast ConvNets Using Group-Wise Brain Damage (2015) — 461 citations