Todd Kulesza

Computer Vision and Pattern Recognition · 2010–2015

A researcher in the Science corpus: 11 papers; trusts 16 researchers and is trusted by 33 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 47 citations into it; #18,639 of 21,575 by trust here, #7,340 by citations
• Artificial Intelligence — 43 citations into it; #6,748 of 35,345 by trust here, #4,466 by citations
• Information Systems and Management — 37 citations into it
• Human-Computer Interaction — 27 citations into it; #555 of 2,253 by trust here, #187 by citations
• Computer Science Applications — 15 citations into it; #1,715 of 1,855 by trust here, #485 by citations
• Evaluation & Benchmarks — 15 citations into it
• Safety, Robustness & Interpretability — 15 citations into it; #895 of 2,251 by trust here, #634 by citations
• Social Psychology — 12 citations into it

Whom Todd Kulesza trusts overall

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

1. Brian Y. Lim
2. Anind K. Dey
3. Simone Stumpf
4. D. Norman
5. Margaret M. Burnett
6. Eric R. Ziegel
7. Ashish Kapoor
8. Jonathan L. Herlocker
9. Joe Tullio
10. Yiming Yang

Who trusts Todd Kulesza overall

1. Giulia Vilone
2. Shane T. Mueller
3. Daniel S. Weld
4. Sina Mohseni
5. Kacper Sokol
6. Minsuk Kahng
7. Besmira Nushi
8. Gagan Bansal
9. Longo Luca
10. Robert R. Hoffman

Papers from Todd Kulesza's perspective

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

1. Explaining collaborative filtering recommendations
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Support-Vector Networks
4. ImageNet classification with deep convolutional neural networks
5. Design of Everyday Things
6. ImageNet: A large-scale hierarchical image database
7. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
8. Gradient-based learning applied to document recognition
9. The Elements of Statistical Learning
10. How it works: a field study of non-technical users interacting with an intelligent system

Their papers

• Power to the People: The Role of Humans in Interactive Machine Learning (2014) — 1,228 citations
• Tell me more?: the effects of mental model soundness on personalizing an intelligent agent (2012) — 290 citations
• Principles of Explanatory Debugging to Personalize Interactive Machine Learning (2015) — 692 citations
• Too much, too little, or just right? Ways explanations impact end users' mental models (2013) — 427 citations
• Mini-crowdsourcing end-user assessment of intelligent assistants: A cost-benefit study (2011) — 13 citations
• You Are the Only Possible Oracle: Effective Test Selection for End Users of Interactive Machine Learning Systems (2014) — 76 citations
• Structured labeling for facilitating concept evolution in machine learning (2014) — 125 citations
• The Role of Explanations in Assessing and Correcting Personalized Intelligent Agents (2012)
• 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
• Explanatory Debugging: Supporting End-User Debugging of Machine-Learned Programs (2010) — 115 citations