Simone Stumpf

Computer Vision and Pattern Recognition · 2002–2021

A researcher in the Science corpus: 17 papers; trusts 52 researchers and is trusted by 41 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 108 citations into it
• Artificial Intelligence — 100 citations into it; #4,978 of 35,345 by trust here, #2,880 by citations
• Human-Computer Interaction — 43 citations into it; #557 of 2,253 by trust here, #185 by citations
• Information Systems and Management — 43 citations into it
• Information Systems — 41 citations into it
• Safety, Robustness & Interpretability — 28 citations into it; #882 of 2,251 by trust here, #530 by citations
• Evaluation & Benchmarks — 26 citations into it
• Representation & Self-Supervised Learning — 17 citations into it

Whom Simone Stumpf trusts overall

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

1. Brian Y. Lim
2. George A. Miller
3. Anind K. Dey
4. Sugato Basu
5. Todd Kulesza
6. Guodong Zhou
7. Bryan Klimt
8. Jonathan L. Herlocker
9. Pearl Huan Z. Pu
10. Jerry Alan Fails

Who trusts Simone Stumpf overall

1. Todd Kulesza
2. Piyawat Lertvittayakumjorn
3. Sina Mohseni
4. Shane T. Mueller
5. Giulia Vilone
6. Saleema Amershi
7. Vivian Lai
8. Brian Y. Lim
9. Francesca Toni
10. Niloofar Zarei

Papers from Simone Stumpf's perspective

Ordered by how strongly the researchers Simone Stumpf 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: A large-scale hierarchical image database
3. ImageNet classification with deep convolutional neural networks
4. ImageNet Large Scale Visual Recognition Challenge
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Attention is All you Need (Neural Information Processing Systems)
7. Gradient-based learning applied to document recognition
8. Long Short-Term Memory
9. Learning Multiple Layers of Features from Tiny Images
10. Microsoft COCO: Common Objects in Context

Their papers

• Tell me more?: the effects of mental model soundness on personalizing an intelligent agent (2012) — 290 citations
• The Role of Explanations on Trust and Reliance in Clinical Decision Support Systems (2015) — 458 citations
• Interacting meaningfully with machine learning systems: Three experiments (2009) — 225 citations
• Principles of Explanatory Debugging to Personalize Interactive Machine Learning (2015) — 692 citations
• Disability-first Dataset Creation: Lessons from Constructing a Dataset for Teachable Object Recognition with Blind and Low Vision Data Collectors (2021) — 50 citations
• ORBIT: A Real-World Few-Shot Dataset for Teachable Object Recognition (2021) — 55 citations
• Too much, too little, or just right? Ways explanations impact end users' mental models (2013) — 427 citations
• XAI—Explainable artificial intelligence (2019) — 1,734 citations
• Explainable AI: The New 42? (2018) — 298 citations
• User Trust in Intelligent Systems: A Journey Over Time (2016) — 86 citations
• You Are the Only Possible Oracle: Effective Test Selection for End Users of Interactive Machine Learning Systems (2014) — 76 citations
• Where Are My Intelligent Assistant's Mistakes? A Systematic Testing Approach (2011) — 21 citations
• Why-oriented end-user debugging of naive Bayes text classification (2011) — 99 citations
• Explaining how to play real-time strategy games (2010) — 9 citations
• Explanatory Debugging: Supporting End-User Debugging of Machine-Learned Programs (2010) — 115 citations
• Toward harnessing user feedback for machine learning (2007) — 180 citations
• Talking about team framing: using argumentation to analyse and support experiential learning in early design episodes (2002) — 132 citations