Evgeniya Ustinova

Computer Vision and Pattern Recognition · 2015–2019

A researcher in the Science corpus: 3 papers; trusts 498 researchers and is trusted by 466 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 97 citations into it; #1,970 of 21,575 by trust here, #1,005 by citations
• Artificial Intelligence — 65 citations into it; #3,279 of 35,345 by trust here, #1,301 by citations
• Representation & Self-Supervised Learning — 48 citations into it; #833 of 3,145 by trust here, #179 by citations
• Signal Processing — 10 citations into it
• Biomedical Engineering — 9 citations into it; #712 of 10,734 by trust here, #213 by citations
• Model Architectures — 8 citations into it
• Evaluation & Benchmarks — 6 citations into it
• Radiology, Nuclear Medicine and Imaging — 6 citations into it

Whom Evgeniya Ustinova trusts overall

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

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

Who trusts Evgeniya Ustinova overall

1. Karl Ridgeway
2. Jérôme Revaud
3. Mingsheng Long
4. Yuhui Yuan
5. Qi Dong
6. Ying Zhang
7. Kuniaki Saito
8. Mohammad Rostami
9. Mahmut Kaya
10. Roman Klokov

Papers from Evgeniya Ustinova's perspective

Ordered by how strongly the researchers Evgeniya Ustinova 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

• Domain-Adversarial Training of Neural Networks (2015) — 11,758 citations
• Learning Deep Embeddings with Histogram Loss (2016) — 368 citations
• Hyperbolic Image Embeddings (2019) — 411 citations