Besmira Nushi
Artificial Intelligence · 2016–2020
A researcher in the Science corpus: 8 papers; trusts 84 researchers and is trusted by 7 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 115 citations into it; #22,799 of 35,345 by trust here, #10,667 by citations
- Safety, Robustness & Interpretability — 44 citations into it; #1,806 of 2,251 by trust here, #1,371 by citations
- Computer Vision and Pattern Recognition — 43 citations into it; #18,469 of 21,575 by trust here, #18,266 by citations
- Natural Language Processing — 26 citations into it
- Evaluation & Benchmarks — 21 citations into it
- Safety Research — 19 citations into it
- Human-Computer Interaction — 17 citations into it; #1,758 of 2,253 by trust here, #276 by citations
- Information Systems — 14 citations into it
Whom Besmira Nushi trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Yoshua Bengio
- Alex Krizhevsky
- Tsung-Yi Lin
- Diederik P. Kingma
- Todd Kulesza
- Kaiming He
- Yann LeCun
- Ilya Sutskever
- Jimmy Ba
Who trusts Besmira Nushi overall
- Sina Mohseni
- Megha Srivastava
- Ece Kamar
- Thomas Liao
- Sorin Mihai Grigorescu
- Niloofar Zarei
- Da-Wei Zhou
- Vivian Lai
- Eric Horvitz
- Gagan Bansal
Papers from Besmira Nushi's perspective
Ordered by how strongly the researchers Besmira Nushi trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- ImageNet Large Scale Visual Recognition Challenge
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Going deeper with convolutions
Their papers
- Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance (2020) — 965 citations
- Guidelines for Human-AI Interaction (2019) — 2,339 citations
- An Empirical Analysis of Backward Compatibility in Machine Learning Systems (2020) — 53 citations
- Beyond Accuracy: The Role of Mental Models in Human-AI Team Performance (2019) — 564 citations
- Software Engineering for Machine Learning: A Case Study (2019) — 1,033 citations
- Updates in Human-AI Teams: Understanding and Addressing the Performance/Compatibility Tradeoff (2019) — 344 citations
- Towards Accountable AI: Hybrid Human-Machine Analyses for Characterizing System Failure (2018) — 155 citations
- On Human Intellect and Machine Failures: Troubleshooting Integrative Machine Learning Systems (2016) — 83 citations