Christin Seifert

Artificial Intelligence · 2004–2022

A researcher in the Science corpus: 7 papers; trusts 509 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 260 citations into it; #21,158 of 35,345 by trust here, #23,731 by citations
• Computer Vision and Pattern Recognition — 174 citations into it; #9,715 of 21,575 by trust here, #11,950 by citations
• Safety, Robustness & Interpretability — 116 citations into it; #1,388 of 2,251 by trust here, #2,078 by citations
• Model Architectures — 27 citations into it
• Natural Language Processing — 25 citations into it
• Representation & Self-Supervised Learning — 25 citations into it
• Evaluation & Benchmarks — 23 citations into it
• Health Informatics — 16 citations into it

Whom Christin Seifert trusts overall

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

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

Who trusts Christin Seifert overall

1. Gabrielle Ras
2. Cynthia Rudin
3. Simone Frintrop
4. Meike Nauta
5. Hugo Larochelle
6. Wenling Shang
7. Marcel A. J. van Gerven

Papers from Christin Seifert's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Q-learning of sequential attention for visual object recognition from informative local descriptors (2005) — 67 citations
• Attentive Object Detection Using an Information Theoretic Saliency Measure (2004) — 34 citations
• 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
• Visualizations of Deep Neural Networks in Computer Vision: A Survey (2017) — 74 citations