Viktoriia Sharmanska

Artificial Intelligence · 2012–2017

A researcher in the Science corpus: 4 papers; trusts 246 researchers and is trusted by 60 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 60 citations into it; #5,460 of 35,345 by trust here, #9,760 by citations
• Computer Vision and Pattern Recognition — 39 citations into it; #5,244 of 21,575 by trust here, #9,722 by citations
• Representation & Self-Supervised Learning — 22 citations into it
• Statistical Learning Theory — 15 citations into it
• Safety, Robustness & Interpretability — 11 citations into it; #899 of 2,251 by trust here, #1,994 by citations
• Optimization & Training — 9 citations into it
• Safety Research — 9 citations into it
• Probabilistic & Bayesian Methods — 8 citations into it

Whom Viktoriia Sharmanska trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Ruslan Salakhutdinov
4. Christoph H. Lampert
5. Alex Krizhevsky
6. Vladimir Naumovich Vapnik
7. Yann LeCun
8. Diederik P. Kingma
9. Max Welling
10. Kaiming He

Who trusts Viktoriia Sharmanska overall

1. Saeid Motiian
2. David Lopez-Paz
3. Christoph H. Lampert
4. Vladimir Naumovich Vapnik
5. Dinesh Jayaraman
6. Kristen Grauman
7. Novi Quadrianto
8. Ziheng Wang
9. Nikolaos Sarafianos
10. Stephan Alaniz

Papers from Viktoriia Sharmanska's perspective

Ordered by how strongly the researchers Viktoriia Sharmanska 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. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
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. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Augmented Attribute Representations (2012) — 90 citations
• Learning to Rank Using Privileged Information (2013) — 199 citations
• Curriculum learning of multiple tasks (2014) — 274 citations
• Recycling Privileged Learning and Distribution Matching for Fairness (2017) — 87 citations