Afra Feyza Akyürek

Artificial Intelligence · 2020–2021

A researcher in the Science corpus: 2 papers; trusts 169 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 71 citations into it; #25,907 of 35,345 by trust here, #19,439 by citations
• Computer Vision and Pattern Recognition — 43 citations into it; #21,419 of 21,575 by trust here, #16,787 by citations
• Natural Language Processing — 28 citations into it
• Representation & Self-Supervised Learning — 22 citations into it; #2,640 of 3,145 by trust here, #2,691 by citations
• Meta & Continual Learning — 19 citations into it; #1,038 of 1,041 by trust here, #897 by citations
• Model Architectures — 12 citations into it

Whom Afra Feyza Akyürek trusts overall

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

1. Geoffrey E. Hinton
2. Kaiming He
3. Yoshua Bengio
4. Ilya Sutskever
5. Alex Krizhevsky
6. Oriol Vinyals
7. Diederik P. Kingma
8. Yann LeCun
9. Brenden M. Lake
10. Ross B. Girshick

Who trusts Afra Feyza Akyürek overall

1. Da-Wei Zhou
2. Keon-Hee Park
3. Yabin Wang
4. Jorge Armando Mendez Mendez
5. Han-Jia Ye
6. Kyungwoo Song
7. Zhiwu Huang
8. Jiayuan Mao
9. Gyeong-Moon Park
10. Jingfeng Yang

Papers from Afra Feyza Akyürek's perspective

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

Their papers

• Learning to Recombine and Resample Data for Compositional Generalization (2020) — 82 citations
• Subspace Regularizers for Few-Shot Class Incremental Learning (2021) — 83 citations