Julie Arnold Shah

Artificial Intelligence · 2011–2022

A researcher in the Science corpus: 13 papers; trusts 219 researchers and is trusted by 59 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 136 citations into it; #4,144 of 35,345 by trust here, #5,458 by citations
• Computer Vision and Pattern Recognition — 50 citations into it
• Safety, Robustness & Interpretability — 42 citations into it; #543 of 2,251 by trust here, #1,186 by citations
• Control and Systems Engineering — 41 citations into it; #1,950 of 8,257 by trust here, #3,682 by citations
• Reinforcement Learning — 41 citations into it
• Social Psychology — 35 citations into it; #2,927 of 15,694 by trust here, #5,937 by citations
• Natural Language Processing — 26 citations into it
• Information Systems — 18 citations into it; #4,085 of 8,771 by trust here, #4,518 by citations

Whom Julie Arnold Shah trusts overall

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

1. Richard S. Sutton
2. W. B. Knox
3. Andrew Y. Ng
4. Andrew Barto
5. Geoffrey E. Hinton
6. Radford M. Neal
7. Guy Hoffman
8. Martin L. Puterman
9. Michael I. Jordan
10. David M. Blei

Who trusts Julie Arnold Shah overall

1. Been Kim
2. Guy Hoffman
3. Stefanos Nikolaidis
4. Giulia Vilone
5. Jangwon Lee
6. Bradley Hayes
7. Nicolas Papernot
8. Nadia Burkart
9. Aaron St. Clair
10. Jongbin Jung

Papers from Julie Arnold Shah's perspective

Ordered by how strongly the researchers Julie Arnold Shah 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. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Human-robot cross-training: Computational formulation, modeling and evaluation of a human team training strategy (2013) — 198 citations
• The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification (2014) — 341 citations
• Improved human-robot team performance using Chaski, A human-inspired plan execution system (2011) — 253 citations
• A Survey of Methods for Safe Human-Robot Interaction (2017) — 502 citations
• Gathering Strength, Gathering Storms: The One Hundred Year Study on Artificial Intelligence (AI100) 2021 Study Panel Report (2022) — 128 citations
• Bayes-Probe: Distribution-Guided Sampling for Prediction Level Sets (2020)
• Improving Robot Controller Transparency Through Autonomous Policy Explanation (2017) — 334 citations
• Inferring Team Task Plans from Human Meetings: A Generative Modeling Approach with Logic-Based Prior (2015) — 25 citations
• Mind the Gap: A Generative Approach to Interpretable Feature Selection and Extraction (2015) — 114 citations
• iBCM: Interactive Bayesian Case Model Empowering Humans via Intuitive Interaction (2015) — 55 citations
• Improved human–robot team performance through cross-training, an approach inspired by human team training practices (2015) — 84 citations
• Fairness in Multi-Agent Sequential Decision-Making (2014) — 70 citations
• Inferring Robot Task Plans from Human Team Meetings: A Generative Modeling Approach with Logic-Based Prior (2013) — 18 citations