Ben Hutchinson

Artificial Intelligence · 2018–2022

A researcher in the Science corpus: 16 papers; trusts 276 researchers and is trusted by 29 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 845 citations into it; #9,852 of 35,345 by trust here, #482 by citations
• Safety, Robustness & Interpretability — 316 citations into it; #502 of 2,251 by trust here, #154 by citations
• Computer Vision and Pattern Recognition — 263 citations into it; #18,264 of 21,575 by trust here, #873 by citations
• Natural Language Processing — 256 citations into it
• Safety Research — 146 citations into it; #2,171 of 5,829 by trust here, #99 by citations
• Evaluation & Benchmarks — 145 citations into it
• Sociology and Political Science — 112 citations into it; #18,015 of 22,756 by trust here, #5,668 by citations
• Information Systems — 94 citations into it

Whom Ben Hutchinson trusts overall

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

1. Geoffrey E. Hinton
2. Emily M. Bender
3. Vinodkumar Prabhakaran
4. Yoshua Bengio
5. Alex Krizhevsky
6. Diederik P. Kingma
7. Alexandra Chouldechova
8. Kaiming He
9. Moritz Hardt
10. Ilya Sutskever

Who trusts Ben Hutchinson overall

1. Paula Czarnowska
2. Emily L. Denton
3. Vinodkumar Prabhakaran
4. Inioluwa Deborah Raji
5. Shaily Bhatt
6. Su Lin Blodgett
7. Emily M. Bender
8. Yogarshi Vyas
9. A. Hanna
10. Angelina Wang

Papers from Ben Hutchinson's perspective

Ordered by how strongly the researchers Ben Hutchinson 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. Attention is All you Need (Neural Information Processing Systems)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• PaLM: Scaling Language Modeling with Pathways (2022) — 8,214 citations
• Scaling Autoregressive Models for Content-Rich Text-to-Image Generation (2022) — 1,537 citations
• Model Cards for Model Reporting (2018) — 2,935 citations
• LaMDA: Language Models for Dialog Applications (2022) — 1,891 citations
• Social Biases in NLP Models as Barriers for Persons with Disabilities (2020) — 378 citations
• Underspecification in Scene Description-to-Depiction Tasks (2022) — 43 citations
• Closing the AI accountability gap: defining an end-to-end framework for internal algorithmic auditing (2020) — 1,364 citations
• Advances and Open Problems in Federated Learning (2019) — 9,019 citations
• Perturbation Sensitivity Analysis to Detect Unintended Model Biases (2019) — 132 citations
• Image Counterfactual Sensitivity Analysis for Detecting Unintended Bias (2019) — 129 citations
• Diversity and Inclusion Metrics in Subset Selection (2020) — 95 citations
• Towards Accountability for Machine Learning Datasets: Practices from Software Engineering and Infrastructure (2020) — 329 citations
• 50 Years of Test (Un)fairness: Lessons for Machine Learning (2018) — 401 citations
• Re-imagining Algorithmic Fairness in India and Beyond (2021) — 269 citations
• Cultural Incongruencies in Artificial Intelligence (2022) — 50 citations
• Evaluation Gaps in Machine Learning Practice (2022) — 101 citations