Pinar E. Donmez

Artificial Intelligence · 2007–2021

A researcher in the Science corpus: 7 papers; trusts 30 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 97 citations into it; #20,385 of 35,345 by trust here, #21,316 by citations
• Natural Language Processing — 88 citations into it
• Computer Vision and Pattern Recognition — 20 citations into it
• Evaluation & Benchmarks — 20 citations into it
• Information Systems — 5 citations into it

Whom Pinar E. Donmez trusts overall

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

1. Leo Breiman
2. Andrew McCallum
3. Ken Lang
4. Dinesh Manocha
5. Simon Tong
6. Nicholas A. Roy
7. Gunnar Rätsch
8. Yoram Baram
9. Peter Auer
10. David D. Lewis

Who trusts Pinar E. Donmez overall

1. Xin Li
2. Ariel Jaffe
3. Zirui Wang
4. Yuhong Guo
5. Boaz Nadler
6. Sanket Vaibhav Mehta
7. Erik Jones

Papers from Pinar E. Donmez's perspective

Ordered by how strongly the researchers Pinar E. Donmez trusts endorse each paper. Same corpus, a different order for every researcher.

1. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
2. ImageNet classification with deep convolutional neural networks
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
7. Gradient-based learning applied to document recognition
8. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. ImageNet: A large-scale hierarchical image database

Their papers

• Unsupervised Supervised Learning I: Estimating Classification and Regression Errors without Labels (2010) — 78 citations
• Building Adaptive Acceptability Classifiers for Neural NLG (2021) — 16 citations
• Getting to Production with Few-shot Natural Language Generation Models (2021) — 17 citations
• Improving Text-to-Text Pre-trained Models for the Graph-to-Text Task (2020)
• Best Practices for Data-Efficient Modeling in NLG:How to Train Production-Ready Neural Models with Less Data (2020) — 10 citations
• Unsupervised Supervised Learning II: Margin-Based Classification without Labels (2011) — 30 citations
• Dual Strategy Active Learning (2007) — 224 citations