Eric Mitchell

Artificial Intelligence · 2020–2023

A researcher in the Science corpus: 8 papers; trusts 291 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 238 citations into it; #18,378 of 35,345 by trust here, #2,112 by citations
• Computer Vision and Pattern Recognition — 106 citations into it; #14,374 of 21,575 by trust here, #9,722 by citations
• Reinforcement Learning — 90 citations into it
• Natural Language Processing — 89 citations into it
• Large Language Models — 58 citations into it; #449 of 799 by trust here, #151 by citations
• Evaluation & Benchmarks — 27 citations into it
• Control and Systems Engineering — 22 citations into it
• Alignment & RLHF — 18 citations into it

Whom Eric Mitchell trusts overall

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

1. Diederik P. Kingma
2. Sergey Levine
3. Geoffrey E. Hinton
4. Chelsea Finn
5. Yoshua Bengio
6. Alex Krizhevsky
7. Kaiming He
8. Ilya Sutskever
9. Yann LeCun
10. Jimmy Ba

Who trusts Eric Mitchell overall

1. Kevin Meng
2. Zexuan Zhong
3. Ce Zheng
4. David Bau
5. Ruiqi Zhang
6. Alex Andonian
7. Hayden S. Helm
8. Arjun Panickssery
9. Sachin Kumar
10. Yonatan Belinkov

Papers from Eric Mitchell's perspective

Ordered by how strongly the researchers Eric Mitchell 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. 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

• On the Opportunities and Risks of Foundation Models (2021) — 6,923 citations
• Fast Model Editing at Scale (2021) — 580 citations
• DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature (2023) — 1,117 citations
• Memory-Based Model Editing at Scale (2022) — 561 citations
• Offline Meta-Reinforcement Learning with Advantage Weighting (2020) — 125 citations
• Direct Preference Optimization: Your Language Model is Secretly a Reward Model (2023) — 9,702 citations
• Just Ask for Calibration: Strategies for Eliciting Calibrated Confidence Scores from Language Models Fine-Tuned with Human Feedback (2023) — 810 citations
• Learning Language-Conditioned Robot Behavior from Offline Data and Crowd-Sourced Annotation (2021) — 189 citations