Emine Yilmaz
Artificial Intelligence · 2006–2025
A researcher in the Science corpus: 14 papers; trusts 85 researchers and is trusted by 55 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 127 citations into it; #3,353 of 35,345 by trust here, #5,187 by citations
- Information Retrieval & Search — 98 citations into it; #618 of 1,022 by trust here, #577 by citations
- Information Systems — 77 citations into it; #742 of 8,771 by trust here, #1,463 by citations
- Evaluation & Benchmarks — 43 citations into it; #1,193 of 3,147 by trust here, #1,603 by citations
- Natural Language Processing — 34 citations into it
- Computer Vision and Pattern Recognition — 28 citations into it; #21,213 of 21,575 by trust here, #12,419 by citations
- Large Language Models — 20 citations into it
- Signal Processing — 12 citations into it; #5,842 of 7,572 by trust here, #6,083 by citations
Whom Emine Yilmaz trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Ellen M. Voorhees
- Chris Buckley
- Bhaskar Mitra
- Nick Craswell
- Stanley F. Chen
- Jimmy J. Lin
- Geoffrey E. Hinton
- Jacob Devlin
- Justin Zobel
- Stephen E. Robertson
Who trusts Emine Yilmaz overall
- Donna K. Harman
- Travis R. Goodwin
- Brian Antonishek
- Ellen M. Voorhees
- Nick Craswell
- Sebastian Hofstätter
- Peter Bailey
- Bhaskar Mitra
- Douglas W. Oard
- Mark Sanderson
Papers from Emine Yilmaz's perspective
Ordered by how strongly the researchers Emine Yilmaz trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Attention is All you Need (Neural Information Processing Systems)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Gradient-based learning applied to document recognition
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Overview of the TREC 2019 deep learning track (2020) — 697 citations
- A simple and efficient sampling method for estimating AP and NDCG (2008) — 328 citations
- A statistical method for system evaluation using incomplete judgments (2006) — 199 citations
- Relevance assessment: are judges exchangeable and does it matter (2008) — 216 citations
- MS MARCO: Benchmarking Ranking Models in the Large-Data Regime (2021) — 85 citations
- Auditing Search Engines for Differential Satisfaction Across Demographics (2017) — 90 citations
- Incorporating Query Term Independence Assumption for Efficient Retrieval and Ranking using Deep Neural Networks (2019) — 31 citations
- TREC Deep Learning Track: Reusable Test Collections in the Large Data Regime (2021) — 73 citations
- Detecting duplicate web documents using clickthrough data (2011) — 25 citations
- Overview of the TREC 2021 Deep Learning Track (2025) — 91 citations
- Overview of the TREC 2020 Deep Learning Track (2021) — 531 citations
- On the Reliability of Test Collections for Evaluating Systems of Different Types (2020) — 21 citations
- A new rank correlation coefficient for information retrieval (2008) — 298 citations
- Estimating average precision with incomplete and imperfect judgments (2006) — 365 citations