Meike Nauta

Artificial Intelligence · 2019–2022

A researcher in the Science corpus: 4 papers; trusts 340 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 242 citations into it; #21,157 of 35,345 by trust here, #23,731 by citations
• Safety, Robustness & Interpretability — 109 citations into it; #1,389 of 2,251 by trust here, #2,078 by citations
• Computer Vision and Pattern Recognition — 106 citations into it; #13,169 of 21,575 by trust here, #18,266 by citations
• Model Architectures — 25 citations into it
• Natural Language Processing — 25 citations into it
• Health Informatics — 16 citations into it
• Evaluation & Benchmarks — 15 citations into it
• Signal Processing — 12 citations into it

Whom Meike Nauta trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Alex Krizhevsky
5. Yoshua Bengio
6. Yann LeCun
7. Ilya Sutskever
8. Karen Simonyan
9. Jia Deng
10. Ross B. Girshick

Who trusts Meike Nauta overall

1. Cynthia Rudin
2. Zhi Chen
3. Haiyang Huang
4. Annemarie Jutte
5. Lesia Semenova

Papers from Meike Nauta's perspective

Ordered by how strongly the researchers Meike Nauta 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. ImageNet: A large-scale hierarchical image database
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Causal Discovery with Attention-Based Convolutional Neural Networks (2019) — 346 citations
• From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI (2022) — 711 citations
• This Looks Like That, Because ... Explaining Prototypes for Interpretable Image Recognition (2020) — 75 citations
• Neural Prototype Trees for Interpretable Fine-grained Image Recognition (2020) — 369 citations