Heike Adel

Artificial Intelligence · 2020–2023

A researcher in the Science corpus: 4 papers; trusts 150 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 157 citations into it; #14,302 of 35,345 by trust here, #21,878 by citations
• Natural Language Processing — 108 citations into it; #2,658 of 5,688 by trust here, #4,682 by citations
• Computer Vision and Pattern Recognition — 59 citations into it; #14,783 of 21,575 by trust here, #17,258 by citations
• Large Language Models — 26 citations into it
• Representation & Self-Supervised Learning — 25 citations into it
• Safety, Robustness & Interpretability — 10 citations into it
• Molecular Biology — 9 citations into it

Whom Heike Adel trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Alex Graves
4. Yann LeCun
5. Diederik P. Kingma
6. Jacob Devlin
7. Ilya Sutskever
8. Alex Krizhevsky
9. Kaiming He
10. Tomas Mikolov

Who trusts Heike Adel overall

1. Sarah Wiegreffe
2. Freda Shi
3. Sufeng Duan
4. Kelechi Ogueji
5. Ana Marasovic
6. Mor Geva

Papers from Heike Adel's perspective

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

1. Attention is All you Need (Neural Information Processing Systems)
2. ImageNet classification with deep convolutional neural networks
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. Gradient-based learning applied to document recognition
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• An Analysis of Simple Data Augmentation for Named Entity Recognition (2020) — 234 citations
• F1 Is Not Enough! Models and Evaluation towards User-Centered Explainable Question Answering (2020) — 23 citations
• A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios (2020) — 396 citations
• SwitchPrompt: Learning Domain-Specific Gated Soft Prompts for Classification in Low-Resource Domains (2023) — 19 citations