Micah Carroll

Artificial Intelligence · 2019–2025

A researcher in the Science corpus: 4 papers; trusts 3 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 197 citations into it
• Reinforcement Learning — 74 citations into it

Whom Micah Carroll trusts overall

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

1. Dean A. Pomerleau
2. Pieter Abbeel
3. Chelsea Finn
4. John Schulman
5. Sergey Levine
6. Geoffrey E. Hinton
7. Anusha Nagabandi
8. Diederik P. Kingma
9. David Silver
10. Max Jaderberg

Papers from Micah Carroll's perspective

Ordered by how strongly the researchers Micah Carroll 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Learning Multiple Layers of Features from Tiny Images
10. GENERATIVE ADVERSARIAL NETS

Their papers

• On the Utility of Learning about Humans for Human-AI Coordination (2019) — 562 citations
• A benchmark of expert-level academic questions to assess AI capabilities (2025) — 349 citations
• Harms from Increasingly Agentic Algorithmic Systems (2023) — 259 citations
• Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback (2023) — 934 citations