Aliaksandra Shysheya

Computer Vision and Pattern Recognition · 2019–2025

A researcher in the Science corpus: 3 papers; trusts 354 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 77 citations into it; #19,459 of 21,575 by trust here, #9,187 by citations
• Artificial Intelligence — 54 citations into it
• Generative Models — 19 citations into it; #1,763 of 1,956 by trust here, #1,054 by citations
• Representation & Self-Supervised Learning — 13 citations into it
• Evaluation & Benchmarks — 12 citations into it
• Optimization & Training — 10 citations into it
• Signal Processing — 9 citations into it
• Media Technology — 8 citations into it

Whom Aliaksandra Shysheya trusts overall

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

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

Who trusts Aliaksandra Shysheya overall

1. Jian Zhao
2. Sangwoo Mo
3. G. Gafni
4. Jing Yang
5. Can Wang
6. Lele Chen
7. Elizaveta Logacheva

Papers from Aliaksandra Shysheya's perspective

Ordered by how strongly the researchers Aliaksandra Shysheya 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. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Few-Shot Adversarial Learning of Realistic Neural Talking Head Models (2019) — 692 citations
• FiT: Parameter Efficient Few-shot Transfer Learning for Personalized and Federated Image Classification (2022) — 37 citations
• A generative model for inorganic materials design (2025) — 567 citations