Sayna Ebrahimi

Computer Vision and Pattern Recognition · 2017–2025

A researcher in the Science corpus: 14 papers; trusts 1,367 researchers and is trusted by 58 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 325 citations into it; #4,391 of 21,575 by trust here, #3,302 by citations
• Artificial Intelligence — 316 citations into it; #5,482 of 35,345 by trust here, #4,205 by citations
• Representation & Self-Supervised Learning — 144 citations into it; #2,954 of 3,145 by trust here, #1,843 by citations
• Generative Models — 68 citations into it
• Meta & Continual Learning — 55 citations into it; #495 of 1,041 by trust here, #140 by citations
• Evaluation & Benchmarks — 43 citations into it
• Model Architectures — 42 citations into it
• Radiology, Nuclear Medicine and Imaging — 40 citations into it

Whom Sayna Ebrahimi trusts overall

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

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

Who trusts Sayna Ebrahimi overall

1. Gobinda Saha
2. Yujun Shi
3. James Smith
4. Samarth Sinha
5. Liyuan Wang
6. Xisen Jin
7. Julio Hurtado
8. Zichen Miao
9. Kaushik Roy
10. Trevor Darrell

Papers from Sayna Ebrahimi's perspective

Ordered by how strongly the researchers Sayna Ebrahimi 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. Gradient-based learning applied to document recognition
7. Long Short-Term Memory
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

• Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders (2018) — 693 citations
• Adversarial Continual Learning (2020) — 236 citations
• Uncertainty-guided Continual Learning with Bayesian Neural Networks (2019) — 214 citations
• Compositional GAN: Learning Image-Conditional Binary Composition (2018) — 103 citations
• DualPrompt: Complementary Prompting for Rehearsal-free Continual Learning (2022) — 880 citations
• Gradient-free Policy Architecture Search and Adaptation (2017) — 30 citations
• Variational Adversarial Active Learning (2019) — 680 citations
• Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities (2025) — 3,609 citations
• Contrastive Test-Time Adaptation (2022) — 401 citations
• Self-Supervised Pretraining Improves Self-Supervised Pretraining (2021) — 137 citations
• Remembering for the Right Reasons: Explanations Reduce Catastrophic Forgetting (2020) — 53 citations
• Generalized Zero-Shot Learning via Aligned Variational Autoencoders (2019) — 24 citations
• Uncertainty-guided Lifelong Learning in Bayesian Networks (2018)
• Compositional GAN: Learning Conditional Image Composition (2018) — 26 citations