Emily Pitler

Artificial Intelligence · 2008–2025

A researcher in the Science corpus: 15 papers; trusts 248 researchers and is trusted by 38 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 247 citations into it; #7,073 of 35,345 by trust here, #5,120 by citations
• Natural Language Processing — 189 citations into it; #1,542 of 5,688 by trust here, #1,000 by citations
• Computer Vision and Pattern Recognition — 69 citations into it; #15,842 of 21,575 by trust here, #13,269 by citations
• Evaluation & Benchmarks — 45 citations into it; #1,614 of 3,147 by trust here, #1,146 by citations
• Safety, Robustness & Interpretability — 39 citations into it; #1,977 of 2,251 by trust here, #1,512 by citations
• Large Language Models — 31 citations into it
• Representation & Self-Supervised Learning — 25 citations into it
• Information Systems — 13 citations into it; #7,156 of 8,771 by trust here, #2,912 by citations

Whom Emily Pitler trusts overall

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

1. Chin-Yew Lin
2. Geoffrey E. Hinton
3. Regina Barzilay
4. Christopher D. Manning
5. Yoshua Bengio
6. Thorsten Joachims
7. Jörg Tiedemann
8. Diederik P. Kingma
9. Ilya Sutskever
10. Andreas Stolcke

Who trusts Emily Pitler overall

1. Christian Stab
2. Chris van der Lee
3. Ziheng Lin
4. Yulia Tsvetkov
5. Kai Hong
6. Iryna Gurevych
7. Najoung Kim
8. R. Ellis
9. Chenhao Tan
10. Albert Gatt

Papers from Emily Pitler's perspective

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

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

Their papers

• Synthetic QA Corpora Generation with Roundtrip Consistency (2019) — 275 citations
• Giving BERT a Calculator: Finding Operations and Arguments with Reading Comprehension (2019) — 102 citations
• Syntactic Data Augmentation Increases Robustness to Inference Heuristics (2020) — 199 citations
• Revisiting Readability: A Unified Framework for Predicting Text Quality (2008) — 510 citations
• The First Multilingual Surface Realisation Shared Task (SR’18): Overview and Evaluation Results (2018) — 65 citations
• CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies (2017) — 354 citations
• Natural Language Processing with Small Feed-Forward Networks (2017) — 39 citations
• A Challenge Set and Methods for Noun-Verb Ambiguity (2018) — 18 citations
• Evaluating the Impact of Model Scale for Compositional Generalization in Semantic Parsing (2022) — 60 citations
• Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities (2025) — 3,609 citations
• Measuring and Reducing Gendered Correlations in Pre-trained Models (2020) — 313 citations
• Scalable Cross Lingual Pivots to Model Pronoun Gender for Translation (2020) — 5 citations
• Universal Dependencies 2.1 (2017) — 425 citations
• Automatic Evaluation of Linguistic Quality in Multi-Document Summarization (2010) — 105 citations
• Automatic sense prediction for implicit discourse relations in text (2009) — 309 citations