Ekin Akyürek

Artificial Intelligence · 2020–2025

A researcher in the Science corpus: 8 papers; trusts 265 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 212 citations into it; #22,512 of 35,345 by trust here, #8,157 by citations
• Natural Language Processing — 93 citations into it
• Computer Vision and Pattern Recognition — 91 citations into it; #21,420 of 21,575 by trust here, #16,787 by citations
• Reinforcement Learning — 33 citations into it
• Representation & Self-Supervised Learning — 32 citations into it; #2,641 of 3,145 by trust here, #2,691 by citations
• Prompting & Reasoning — 23 citations into it
• Model Architectures — 22 citations into it
• Meta & Continual Learning — 20 citations into it; #1,039 of 1,041 by trust here, #897 by citations

Whom Ekin Akyürek trusts overall

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

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

Who trusts Ekin Akyürek overall

1. Michael R Douglas
2. Jorge Armando Mendez Mendez
3. Qingxiu Dong
4. Jiayuan Mao
5. Jingfeng Yang
6. Kwangjun Ahn
7. Eric Eaton
8. Angeliki Giannou
9. Lei Li
10. Da-Wei Zhou

Papers from Ekin Akyürek's perspective

Ordered by how strongly the researchers Ekin 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. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• Pre-Trained Language Models for Interactive Decision-Making (2022) — 334 citations
• Learning to Recombine and Resample Data for Compositional Generalization (2020) — 82 citations
• What learning algorithm is in-context learning? Investigations with linear models (2022) — 748 citations
• Tracing Knowledge in Language Models Back to the Training Data (2022) — 29 citations
• Reasoning or Reciting? Exploring the Capabilities and Limitations of Language Models Through Counterfactual Tasks (2023) — 353 citations
• OpenAI GPT-5 System Card (2025) — 667 citations
• Towards Tracing Knowledge in Language Models Back to the Training Data (2022) — 71 citations
• Subspace Regularizers for Few-Shot Class Incremental Learning (2021) — 83 citations