Evgenia Rusak

Computer Vision and Pattern Recognition · 2019–2020

A researcher in the Science corpus: 4 papers; trusts 313 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 126 citations into it
• Artificial Intelligence — 120 citations into it
• Safety, Robustness & Interpretability — 57 citations into it
• Evaluation & Benchmarks — 28 citations into it
• Optimization & Training — 19 citations into it
• Model Architectures — 17 citations into it
• Media Technology — 9 citations into it
• Industrial and Manufacturing Engineering — 7 citations into it

Whom Evgenia Rusak trusts overall

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

1. Kaiming He
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Dan Hendrycks
5. Diederik P. Kingma
6. Christian Szegedy
7. Richard Zhang
8. Ross B. Girshick
9. Yoshua Bengio
10. Karen Simonyan

Who trusts Evgenia Rusak overall

1. Shuaicheng Niu
2. Dequan Wang
3. Linjie Li
4. Marvin Zhang
5. Steffen Schneider
6. Cristina Nader Vasconcelos
7. Omid Nejati Manzari
8. Jiaxiang Wu
9. Yusuke Iwasawa
10. Evan Shelhamer

Papers from Evgenia Rusak's perspective

Ordered by how strongly the researchers Evgenia Rusak 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. ImageNet: A large-scale hierarchical image database
5. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
6. Attention is All you Need (Neural Information Processing Systems)
7. Gradient-based learning applied to document recognition
8. Long Short-Term Memory
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Improving robustness against common corruptions by covariate shift adaptation (2020) — 651 citations
• A Simple Way to Make Neural Networks Robust Against Diverse Image Corruptions (2020) — 243 citations
• Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming (2019) — 574 citations
• Increasing the robustness of DNNs against image corruptions by playing the Game of Noise (2020) — 66 citations