Stella Frank

Artificial Intelligence · 2016–2022

A researcher in the Science corpus: 8 papers; trusts 241 researchers and is trusted by 76 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 162 citations into it; #3,204 of 35,345 by trust here, #4,393 by citations
• Natural Language Processing — 142 citations into it; #2,920 of 5,688 by trust here, #2,422 by citations
• Computer Vision and Pattern Recognition — 81 citations into it; #3,546 of 21,575 by trust here, #3,613 by citations
• Evaluation & Benchmarks — 62 citations into it; #849 of 3,147 by trust here, #1,355 by citations
• Multimodal Learning — 24 citations into it; #127 of 1,211 by trust here, #322 by citations
• Model Architectures — 19 citations into it
• Representation & Self-Supervised Learning — 11 citations into it
• Optimization & Training — 9 citations into it

Whom Stella Frank trusts overall

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

1. Philipp Koehn
2. Franz Joseph Och
3. Kaiming He
4. Yoshua Bengio
5. Desmond Elliott
6. Geoffrey E. Hinton
7. Alex Krizhevsky
8. Ilya Sutskever
9. Diederik P. Kingma
10. Michael J. Denkowski

Who trusts Stella Frank overall

1. Desmond Elliott
2. Ozan Caglayan
3. Jean-Benoit Delbrouck
4. Loïc Barrault
5. Pranava Swaroop Madhyastha
6. Lucia Specia
7. Po-Yao (Bernie) Huang
8. Iacer Calixto
9. Shaowei Yao
10. Jamie Ryan Kiros

Papers from Stella Frank's perspective

Ordered by how strongly the researchers Stella Frank 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Multi30K: Multilingual English-German Image Descriptions (2016) — 660 citations
• Findings of the Third Shared Task on Multimodal Machine Translation (2018) — 160 citations
• A Shared Task on Multimodal Machine Translation and Crosslingual Image Description (2016) — 255 citations
• Findings of the Second Shared Task on Multimodal Machine Translation and Multilingual Image Description (2017) — 233 citations
• The QT21/HimL Combined Machine Translation System (2016) — 17 citations
• Challenges and Strategies in Cross-Cultural NLP (2022) — 290 citations
• Can Language Models Encode Perceptual Structure Without Grounding? A Case Study in Color (2021) — 178 citations
• ILLC-UvA Adaptation System (Scorpio) at WMT’16 IT-DOMAIN Task (2016) — 4 citations