Clara Vania

Artificial Intelligence · 2017–2021

A researcher in the Science corpus: 8 papers; trusts 321 researchers and is trusted by 18 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 251 citations into it; #9,206 of 35,345 by trust here, #4,829 by citations
• Natural Language Processing — 224 citations into it; #1,988 of 5,688 by trust here, #1,170 by citations
• Evaluation & Benchmarks — 119 citations into it
• Computer Vision and Pattern Recognition — 89 citations into it
• Large Language Models — 34 citations into it
• Representation & Self-Supervised Learning — 29 citations into it
• Model Architectures — 12 citations into it
• General Social Sciences — 11 citations into it

Whom Clara Vania trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Diederik P. Kingma
4. Alex Graves
5. Tomas Mikolov
6. Ilya Sutskever
7. Alex Krizhevsky
8. Jacob Devlin
9. Kyunghyun Cho
10. Samuel R. Bowman

Who trusts Clara Vania overall

1. Rafael-Michael Karampatsis
2. Eric Michael Smith
3. Adina Williams
4. Irene Solaiman
5. Charles Sutton
6. Julian Martin Eisenschlos
7. Yonatan Belinkov
8. Samuel R. Bowman
9. Michael A. Hedderich
10. Sunipa Dev

Papers from Clara Vania's perspective

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

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

Their papers

• CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models (2020) — 993 citations
• Intermediate-Task Transfer Learning with Pretrained Language Models: When and Why Does It Work? (2020) — 210 citations
• What Ingredients Make for an Effective Crowdsourcing Protocol for Difficult NLU Data Collection Tasks? (2021) — 38 citations
• English Intermediate-Task Training Improves Zero-Shot Cross-Lingual Transfer Too (2020) — 69 citations
• From Characters to Words to in Between: Do We Capture Morphology? (2017) — 103 citations
• Asking Crowdworkers to Write Entailment Examples: The Best of Bad Options (2020) — 12 citations
• Comparing Test Sets with Item Response Theory (2021) — 64 citations
• Edinburgh Research Explorer A systematic comparison of methods for low-resource dependency parsing on genuinely low-resource languages