Margarita Osadchy

Computer Vision and Pattern Recognition · 2004–2020

A researcher in the Science corpus: 4 papers; trusts 215 researchers and is trusted by 377 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 76 citations into it; #2,116 of 21,575 by trust here, #6,954 by citations
• Artificial Intelligence — 53 citations into it; #26,518 of 35,345 by trust here, #28,481 by citations
• Efficiency & Compression — 42 citations into it; #912 of 1,264 by trust here, #1,174 by citations
• Optimization & Training — 14 citations into it
• Model Architectures — 9 citations into it
• Scaling & Learning Theory — 8 citations into it
• Computational Mechanics — 5 citations into it

Whom Margarita Osadchy trusts overall

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

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

Who trusts Margarita Osadchy overall

1. Yann LeCun
2. Clément Farabet
3. Xiaogang Wang
4. Alexander Toshev
5. Yoshua Bengio
6. Huaizu Jiang
7. Luc Van Gool
8. Andrew Zisserman
9. Cha Zhang
10. Massimiliano Patacchiola

Papers from Margarita Osadchy's perspective

Ordered by how strongly the researchers Margarita Osadchy 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. 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. Learning Multiple Layers of Features from Tiny Images
10. Going deeper with convolutions

Their papers

• Synergistic Face Detection and Pose Estimation with Energy-Based Models (2004) — 431 citations
• Data-Independent Neural Pruning via Coresets (2019) — 67 citations
• Data-Independent Structured Pruning of Neural Networks via Coresets (2020) — 28 citations
• Deep Convolutional Neural Networks for Raman Spectrum Recognition: A Unified Solution (2017) — 367 citations