Nina Poerner

Artificial Intelligence · 2019

A researcher in the Science corpus: 4 papers; trusts 167 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 80 citations into it; #13,149 of 35,345 by trust here, #18,586 by citations
• Natural Language Processing — 75 citations into it; #2,479 of 5,688 by trust here, #3,687 by citations
• Representation & Self-Supervised Learning — 38 citations into it; #2,555 of 3,145 by trust here, #3,089 by citations
• Large Language Models — 19 citations into it; #547 of 799 by trust here, #677 by citations
• Computer Vision and Pattern Recognition — 18 citations into it
• Evaluation & Benchmarks — 9 citations into it
• Management Science and Operations Research — 6 citations into it
• Model Architectures — 6 citations into it

Whom Nina Poerner trusts overall

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

1. Tomas Mikolov
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Ilya Sutskever
6. John Wieting
7. Sepp Hochreiter
8. Quoc V. Le
9. Alex Krizhevsky
10. Jimmy Ba

Who trusts Nina Poerner overall

1. Zhengbao Jiang
2. Hinrich Schütze
3. Timo Schick
4. Tara Safavi
5. Ethan Perez
6. Nora Kassner
7. Katikapalli Subramanyam Kalyan
8. Zexuan Zhong
9. Yanai Elazar
10. Yanda Chen

Papers from Nina Poerner's perspective

Ordered by how strongly the researchers Nina Poerner 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. Attention is All you Need (Neural Information Processing Systems)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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

• E-BERT: Efficient-Yet-Effective Entity Embeddings for BERT (2019) — 168 citations
• Multi-View Domain Adapted Sentence Embeddings for Low-Resource Unsupervised Duplicate Question Detection (2019) — 15 citations
• Sentence Meta-Embeddings for Unsupervised Semantic Textual Similarity (2019) — 21 citations
• BERT is Not a Knowledge Base (Yet): Factual Knowledge vs. Name-Based Reasoning in Unsupervised QA (2019) — 80 citations