Daniel Deutsch

Artificial Intelligence · 2020–2022

A researcher in the Science corpus: 5 papers; trusts 92 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 114 citations into it; #9,433 of 35,345 by trust here, #13,760 by citations
• Natural Language Processing — 109 citations into it
• Evaluation & Benchmarks — 83 citations into it; #1,367 of 3,147 by trust here, #2,166 by citations
• Computer Vision and Pattern Recognition — 22 citations into it; #11,558 of 21,575 by trust here, #15,529 by citations

Whom Daniel Deutsch trusts overall

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

1. Chin-Yew Lin
2. Ani Nenkova
3. Ramesh Nallapati
4. Alexander R. Fabbri
5. Ori Shapira
6. Maxime Peyrard
7. Pranav Rajpurkar
8. Yvette Graham
9. Tianyi Zhang
10. Geoffrey E. Hinton

Who trusts Daniel Deutsch overall

1. Alexander R. Fabbri
2. Yixin Liu
3. Markus Freitag
4. Dan Roth
5. Sebastian Gehrmann
6. Shiyue Zhang
7. Tanya Goyal
8. Rotem Dror
9. Ricardo Rei
10. Pierre Colombo

Papers from Daniel Deutsch's perspective

Ordered by how strongly the researchers Daniel Deutsch 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. ImageNet classification with deep convolutional neural networks
6. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
7. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
8. ImageNet: A large-scale hierarchical image database
9. Gradient-based learning applied to document recognition
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• GEMv2: Multilingual NLG Benchmarking in a Single Line of Code (2022) — 43 citations
• Towards Question-Answering as an Automatic Metric for Evaluating the Content Quality of a Summary (2020) — 145 citations
• Understanding the Extent to which Content Quality Metrics Measure the Information Quality of Summaries (2021) — 33 citations
• A Statistical Analysis of Summarization Evaluation Metrics Using Resampling Methods (2021) — 96 citations
• Re-Examining System-Level Correlations of Automatic Summarization Evaluation Metrics (2022) — 53 citations