Robert R. Hoffman

Artificial Intelligence · 1989–2019

A researcher in the Science corpus: 30 papers; trusts 77 researchers and is trusted by 25 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 229 citations into it; #7,385 of 35,345 by trust here, #6,064 by citations
• Social Psychology — 93 citations into it; #8,742 of 15,694 by trust here, #4,757 by citations
• Safety, Robustness & Interpretability — 84 citations into it; #1,529 of 2,251 by trust here, #1,110 by citations
• Computer Vision and Pattern Recognition — 63 citations into it
• Experimental and Cognitive Psychology — 58 citations into it
• Developmental and Educational Psychology — 54 citations into it
• Human-Computer Interaction — 46 citations into it; #2,049 of 2,253 by trust here, #1,041 by citations
• Cognitive Neuroscience — 37 citations into it; #3,952 of 10,852 by trust here, #4,925 by citations

Whom Robert R. Hoffman trusts overall

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

1. Tania Lombrozo
2. William J. Clancey
3. Gary Klein
4. William R. Swartout
5. Edward H. Shortliffe
6. Dedre Gentner
7. Tim Miller
8. Keith J. Holyoak
9. D. Griffith
10. Mary T. Dzindolet

Who trusts Robert R. Hoffman overall

1. D. Gunning
2. Sina Mohseni
3. Shane T. Mueller
4. Gary Klein
5. David William Aha
6. Niloofar Zarei
7. Sarah Wiegreffe
8. Ben Shneiderman
9. Peter A. Hancock
10. Eric D. Ragan

Papers from Robert R. Hoffman's perspective

Ordered by how strongly the researchers Robert R. Hoffman 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. ImageNet Large Scale Visual Recognition Challenge
9. Going deeper with convolutions
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Work-arounds, Make-work, and Kludges (2003) — 170 citations
• Measuring Resilience (2017) — 127 citations
• Explaining Explanation, Part 1: Theoretical Foundations (2017) — 85 citations
• Making Sense of Sensemaking 1: Alternative Perspectives (2006) — 888 citations
• Seven Cardinal Virtues of Human-Machine Teamwork: Examples from the DARPA Robotic Challenge (2014) — 47 citations
• Ten Challenges for Making Automation a "Team Player" in Joint Human-Agent Activity (2004) — 662 citations
• Theory → Concepts → Measures but Policies → Metrics (2018) — 7 citations
• Making Sense of Sensemaking 2: A Macrocognitive Model (2006) — 609 citations
• Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AI (2019) — 323 citations
• Explaining Explanation, Part 4: A Deep Dive on Deep Nets (2018) — 56 citations
• Adaptive Skill as the Conditio Sine Qua Non of Expertise (2018) — 111 citations
• Metrics for Explainable AI: Challenges and Prospects (2018) — 907 citations
• Explaining Explanation, Part 2: Empirical Foundations (2017) — 52 citations
• Minding the Weather: How Expert Forecasters Think (2017)
• A Taxonomy of Emergent Trusting in the Human–Machine Relationship (2017) — 64 citations
• Epistemic curiosity and self-regulation (2015) — 100 citations
• The Cambridge handbook of applied perception research (2015) — 88 citations
• Influencing Preferences for Different Types of Causal Explanation of Complex Events (2014) — 17 citations
• Trust in Automation (2013) — 120 citations
• Accelerated Expertise: Training for High Proficiency in a Complex World (2013)